Abstracts
In this study we attempted to access further information on the geographical distribution of the endangered lizard Liolaemus lutzae, estimating its potential distribution through the maximum entropy algorithm. For this purpose, we related its points of occurrence with matrices of environmental variables. After examining the correlation between environmental matrices, we selected 10 for model construction. The main variables influencing the current geographic distribution of L. lutzae were the diurnal temperature range and altitude. The species endemism seemed to be a consequence of a reduction of the original distribution area. Alternatively, the resulting model may reflect the geographic distribution of an ancestral lineage, since the model selected areas of occurrence of the two other species of Liolaemus from Brazil (L. arambarensis and L. occipitalis), all living in sand dune habitats and having psamophilic habits. Due to the high loss rate of habitat occupied by the species, the conservation and recovery of the remaining areas affected by human actions is essential.
lizards; coastal environment; ecological modeling; biogeography; maximum entropy
No presente estudo buscamos acessar informações adicionais acerca da distribuição geográfica do lagarto Liolaemus lutzae, estimando sua distribuição potencial através do algoritmo de máxima entropia. Para tanto, relacionamos os pontos de ocorrência da espécie com matrizes de variáveis ambientais. Após análise da correlação entre as matrizes ambientais, selecionamos 10 variáveis não correlacionadas para a construção do modelo. As principais variáveis que influenciam a distribuição geográfica atual de L. lutzae foram a amplitude média diurna de temperatura e a altitude. O endemismo atual da espécie parece ser consequência da redução da área de ocorrência original. Alternativamente, o modelo resultante pode refletir a distribuição geográfica de uma linhagem ancestral, devido à seleção das áreas de ocorrência das outras espécies de Liolaemus com registro no Brasil (L. arambarensis e L. occipitalis), todas vivendo em ambientes de dunas arenosas e possuindo hábitos psamófilos. Considerando a grande taxa de perda do habitat ocupado pela espécie, torna-se imprescindível a conservação dos remanescentes e a recuperação de áreas afetadas pelas ações humanas.
lagartos; ambiente litorâneo; modelagem ecológica; biogeografia; entropia máxima
1. Introduction
Ecological modelling may be used to estimate the potential geographical
distribution of species based on environmental attributes that are related to the
elements necessary to allow the occurrence and distribution of a particular organism
(Peterson, 2001PETERSON, AT., 2001. Predicting species' geographic
distributions based on ecological niche modelling. The Condor, vol. 103, no. 3,
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993-1009.
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; Giovanelli et al., 2010GIOVANELLI, JGR., SIQUEIRA, MF., HADDAD, CFB. and ALEXANDRINO, J.,
2010. Modelling a spatially restricted distribution in the Neotropics: How the
size of calibration area affects the performance of five presence-only methods.
Ecological Modelling, vol. 221, no. 2, p. 215-224.
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http://dx.doi.org/10.1016/j.ecolmodel.20...
). Different
techniques for characterising the geographical distribution of species relate the
occurrence records of species with environmental data, and have been widely applied
in different geographical scales (Guisan and
Thuiller, 2005GUISAN, A. and THUILLER, W., 2005. Predicting species distributions:
offering more than simple habitat models. Ecology Letters, vol. 8, no. 9, p.
993-1009.
http://dx.doi.org/10.1111/j.1461-0248.2005.00792.x.
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; Phillips et al.,
2006PHILLIPS, SJ., ANDERSON, RP. and SCHAPIRE, RE., 2006. Maximum
entropy modelling of species geographic distributions. Ecological Modelling,
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AT., 2007. Predicting species distributions from small numbers of occurrence
records: a test case using cryptic geckos in Madagascar. Journal of
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).
Applicability of such techniques include easier detection of unknown distribution
areas for a species having few distributional records (Bourg et al., 2005BOURG, NA., MCSHEA, WJ. and GILL, DE., 2005. Putting a cart before
the search: successful habitat prediction for a rare forest herb. Ecology, vol.
86, no. 10, p. 2793-2804.
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), potential impacts of environmental
changes on organisms (e.g., Rocha et al.,
2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
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BASTIAANS, E., VILLAGRÁN-SANTA CRUZ, M., LARA-RESENDIZ, R.,
MARTÍNEZ-MÉNDEZ, N., CALDERÓN-ESPINOSA, ML., MEZA-LÁZARO,
RN., GADSDEN, H., AVILA, LJ., MORANDO, M., DE LA RIVA, IJ., VICTORIANO
SEPULVEDA, P., ROCHA, CFD., IBARGÜENGOYTÍA, N., AGUILAR PUNTRIANO, C.,
MASSOT, M., LEPETZ, V., OKSANEN, TA., CHAPPLE, DG., BAUER, AM., BRANCH, WR.,
CLOBERT, J. and SITES, JW Jr., 2010. Erosion of lizard diversity by climate
change and altered thermal niches. Science, vol. 328, no. 5980, p. 894-899.
http://dx.doi.org/10.1126/science.1184695.
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),
the support when planning conservation policies (e.g., Araújo and Williams, 2000ARAÚJO, MB. and WILLIAMS, PH., 2000. Selecting areas for
species persistence using occurrence data. Biological Conservation, vol. 96, no.
3, p. 331-345.
http://dx.doi.org/10.1016/S0006-3207(00)00074-4.
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) and to preview potential
expansion in distribution of invasive alien species (e.g., Thuiller et al., 2005THUILLER, W., RICHARDSON, DM., PYSEK, P., MIDGLEY, GF., HUGHES, GO.
and ROUGET, M., 2005. Niche-based modelling as a tool for predicting the risk of
alien plant invasions at a global scale. Global Change Biology, vol. 11, no. 12,
p. 2234-2250.
http://dx.doi.org/10.1111/j.1365-2486.2005.001018.x.
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; Rödder et al., 2008RÖDDER, D., SOLÉ, M. and BÖHME, W., 2008. Predicting
the potential distribution of two alien invasive Housegeckos (Gekkonidae:
Hemidactylus frenatus, Hemidactylus mabouia). North-Western Journal of
Zoology, vol. 4, p. 236-246.). Species distribution modelling
is commonly based substantially on climatic variables that have strong association
with broad-scale geographic patterns of species richness (Hawkins et al., 2003HAWKINS, BA., FIELD, R., CORNELL, HV., CURRIE, DJ., GUÉGAN,
JF., KAUFMAN, DM., KERR, JT., MITTELBACH, GG., OBERDORFF, T., O'BRIEN, EM.,
PORTER, EE. and TURNER, JRG., 2003. Energy, water, and broad-scale geographic
patterns of species richness. Ecology, vol. 84, no. 12, p. 3105-3117.
http://dx.doi.org/10.1890/03-8006
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and Jetz, 2007BUCKLEY, LB. and JETZ, W., 2007. Environmental and historical
constraints on global patterns of amphibian richness. Proceedings of The Royal
Society/Biological Sciences, vol. 274, no. 1614, p. 1167-1173.
http://dx.doi.org/10.1098/rspb.2006.0436.
PMid:17327208
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). Distribution maps generated by these techniques are
widely accepted among the scientific community and are increasingly being used in a
range of research fields (Franklin,
2010FRANKLIN, J., 2010. Moving beyond static species distribution models
in support of conservation biogeography. Diversity & Distributions, vol.
16, no. 3, p. 321-330.
http://dx.doi.org/10.1111/j.1472-4642.2010.00641.x.
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). On the other hand, range maps generated by distribution modelling
are expected to overpredict the distributional limits of species, predicting
presence where it is known to be truly absent (errors of commission). This
occurs because many modelling methods are unable to evaluate absences generated by
evolutionary history of species, dispersal limitations, and biotic interactions with
other species (Graham and Hijmans, 2006GRAHAM, CH. and HIJMANS, RJ., 2006. A comparison of methods for
mapping species ranges and species richness. Global Ecology and Biogeography,
vol. 15, no. 6, p. 578-587.
http://dx.doi.org/10.1111/j.1466-8238.2006.00257.x.
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;
Pineda and Lobo, 2009PINEDA, E. and LOBO, JM. 2009. Assessing the accuracy of species
distribution models to predict amphibian species richness patterns. Journal of
Animal Ecology, vol. 2009, no. 78, p. 182-190.).
The genus Liolaemus Wiegmann, 1834 comprises more than
220 species distributed throughout South America and is known to have the greatest
climatic, latitudinal and altitudinal (from sea level to over 5000 m)
distributions among lizards. However, only three species, all of them sand-dwellings
(Etheridge, 2000ETHERIDGE, R., 2000. A review of lizards of the Liolaemus wiegmannii
group (Squamata, Iguania, Tropiduridae), and a history of
morphological change in the sand-dwelling species. Herpetological Monographs,
vol. 14, no. 2000, p. 293-352.
http://dx.doi.org/10.2307/1467049.
http://dx.doi.org/10.2307/1467049...
), are
presently known to occur in Brazil: L. arambarensis Verrastro,
Veronese, Bujes and Dias-Filho, 2003 and L. occipitalis Boulenger,
1885, both from coastal sandy habitats from southern Brazil, and Liolaemus
lutzae Mertens, 1938, endemic to the restinga habitats of Rio de
Janeiro State, southeastern Brazil (Rocha et
al., 2005ROCHA, CFD., VAN SLUYS, M., BERGALLO, HG. and ALVES, MAS., 2005.
Endemic and threatened tetrapods in the restingas of the biodiversity corridors
of Serra do Mar and of the Central da Mata Atlântica in eastern Brazil.
Brazilian Journal of Biology, vol. 65, no. 1, p. 159-168.
http://dx.doi.org/10.1590/S1519-69842005000100019.
PMid:16025914
http://dx.doi.org/10.1590/S1519-69842005...
). Liolaemus lutzae is a small
sand-dwelling lizard (51-80 mm snout-vent length when adult; Rocha, 1992).
Its distribution is markedly narrow and comprises from Restinga da Marambaia
(23°04′S, 43°52′W) to Cabo Frio
(22°51′S, 41°59′W), inserted in the Atlantic
Rainforest domain (for details on ‘restinga’ environment, see
Araujo and Henriques, 1984ARAUJO, DSD. and HENRIQUES, RPB., 1984. Análise florística
das restingas do Estado do Rio de Janeiro. In LACERDA, LD., ARAÚJO, DSD.,
CERQUEIRA, R. and TURCQ, B. Restingas: Origem, Estrutura e Processos
Niterói: CEUFF. p. 159-194.; Suguio and Tessler, 1984SUGUIO, K. and TESSLER, MG., 1984. Planícies de cordões
litorâneos quaternários do Brasil: origem e nomenclatura. In LACERDA,
LD., ARAÚJO, DSD., CERQUEIRA, R. and TURCQ, B. Restingas: Origem, Estrutura
e Processos. Niterói: CEUFF. p. 15-26.; Pereira and Araújo, 2000PEREIRA, OJ. and ARAÚJO, DSD., 2000. Análise
Florística das Restingas dos Estados do Espírito Santo e Rio de
Janeiro. In ESTEVES F.A., LACERDA LD. Ecologia de Restingas e Lagoas Costeiras.
Macaé: NUPEM/UFRJ. p. 23-61.). In these areas, L.
lutzae is mainly found in a thin strip of approximately 50 to 100 m
wide of beach vegetation (Vanzolini and
Ab'Saber, 1968VANZOLINI, PE. and AB'SABER, AN., 1968. Divergence rate in
South American lizards of the genus Liolaemus (Sauria, Iguanidae).
Papéis Avulsos de Zoologia, vol. 21, p. 205-208.; Rocha, 1991; Rocha
et al., 2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
) where males and females share the relatively small
area of habitat for their ecological tasks such as thermoregulation (Rocha,
1995), space use (Rocha, 1999) and feeding activities (Rocha,
1989). This species is currently considered to be threatened with extinction
even in State level (Governmental Decree from 5 june 1998; Bergallo et al., 1998BERGALLO, HG., ROCHA, CFD., SLUYS, MV., ALVES, MAS., MOULTON, TP.
and TANIZAKI, K. Lista da Fauna Ameaçada de Extinção do Estado do
Rio de Janeiro. Diário Oficial do Estado do Rio de Janeiro, Rio de Janeiro,
05 jun. 1998, v. XXIV, p. 9-16.), as in federal
level (Brasil, 2003) and global level (IUCN, 2012), as result of
the intensive and extensive destruction of restinga habitats (Rocha et al., 2007ROCHA, CFD., BERGALLO, HG., VAN SLUYS, M., ALVES, MAS. and JAMEL,
CE., 2007. The remnants of restinga habitats in the brazilian Atlantic Forest of
Rio de Janeiro state, Brazil: habitat loss and risk of disappearance. Brazilian
Journal of Biology, vol. 67, no. 2, p. 263-273.
http://dx.doi.org/10.1590/S1519-69842007000200011.
PMid:17876436
http://dx.doi.org/10.1590/S1519-69842007...
).
In the present study, we evaluated whether Liolaemus lutzae, currently restricted to restinga areas in the state of Rio de Janeiro, would have the potential to occur in other areas of Brazil, and which bioclimatic variables would have greater influence on the limits of its distribution.
2. Material and Methods
We used the occurrence records of Liolaemus lutzae from
Rocha et al. (2009a)ROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
(Table 1, Figure 1), excluding the population from Praia das Neves
(state of Espírito Santo), due to this population resulting from an
experimental introduction in the area and not within the original species
distribution (see Soares and Araújo,
2008SOARES, AHB. and ARAÚJO, AFB., 2008. Experimental introduction
of Liolaemus lutzae (Squamata, Iguanidae) in Praia das Neves, State of
Espírito Santo, Brazil: a descriptive study 18 years later. Revista
Brasileira de Zoologia, vol. 25, no. 4, p. 640-646.
http://dx.doi.org/10.1590/S0101-81752008000400009.
http://dx.doi.org/10.1590/S0101-81752008...
).
Records of occurrence of the lizard Liolaemus lutzae, endemic to the state of Rio de Janeiro, Brasil. Grey areas represent the remnants of restinga habitats according to the “SOS Mata Atlântica Organization”. 1. Marambaia, 2. Grumari, 3. Prainha, 4. Macumba, 5. Recreio, 6. Barra da Tijuca, 7-10. Piratininga, Camboinhas, Itaipu e Itacoatiara, 11. Itaipuaçu, 12. Barra de Maricá, 13. Ponta Negra, 14. Jaconé, 15. Barra Nova, 16. Itaúna/Jacarepiá, 17. Pernambuca, 18. Massambaba, 19. Figueiras, 20. Praia Grande, 21. Praia dos Anjos, 22. Praia do Foguete, 23. Praia do Forte, 24. Praia das Conchas, 25. Praia do Peró. Modified from Rocha et al. (2009a)ROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and threatened Liolaemus lutzae (Squamata: Liolaemidae): current geographic distribution and areas of occurrence with estimated population densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460. http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009... .
Localities, municipalities and coordinates with records of Liolaemus lutzae used for potential distribution modelling. Modified from Rocha et al. (2009a)ROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and threatened Liolaemus lutzae (Squamata: Liolaemidae): current geographic distribution and areas of occurrence with estimated population densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460. http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009... .
To create the potential distribution model, we used the software MaxEnt
3.2.3. Despite the availability of different methods for potential distribution
modelling, some studies have shown the Maximum Entropy as one of the best performing
algorithms (e.g., Elith et al., 2006ELITH, J., GRAHAM, CH., ANDERSON, RP., DUDÍK, M., FERRIER, S.,
GUISAN, A., HIJMANS, RJ., HUETTMANN, F., LEATHWICK, JR., LEHMANN, A., LI, J.,
LOHMANN, LG., LOISELLE, BA., MANION, G., MORITZ, C., NAKAMURA, M., NAKAZAWA, Y.,
OVERTON, JM., PETERSON, AT., PHILLIPS, SJ., RICHARDSON, K., SCACHETTI-PEREIRA,
R., SCHAPIRE, RE., SOBERÓN, J., WILLIAMS, S., WISZ, MS. and ZIMMERMANN,
NE., 2006. Novel methods improve prediction of species' distributions from
occurrence data. Ecography, vol. 29, no. 2, p. 129-151.
http://dx.doi.org/10.1111/j.2006.0906-7590.04596.x.
http://dx.doi.org/10.1111/j.2006.0906-75...
;
Costa et al., 2010COSTA, GC., NOGUEIRA, C., MACHADO, RB. and COLLI, GR., 2010.
Sampling bias and the use of ecological niche modelling in conservation
planning: a field evaluation in a biodiversity hotspot. Biodiversity and
Conservation, vol. 19, no. 3, p. 883-899.
http://dx.doi.org/10.1007/s10531-009-9746-8.
http://dx.doi.org/10.1007/s10531-009-974...
; Giovanelli et al., 2010GIOVANELLI, JGR., SIQUEIRA, MF., HADDAD, CFB. and ALEXANDRINO, J.,
2010. Modelling a spatially restricted distribution in the Neotropics: How the
size of calibration area affects the performance of five presence-only methods.
Ecological Modelling, vol. 221, no. 2, p. 215-224.
http://dx.doi.org/10.1016/j.ecolmodel.2009.10.009.
http://dx.doi.org/10.1016/j.ecolmodel.20...
). The model is estimated from data
of presence, and generates pseudo-absence data for the calibration (Pawar et al., 2007PAWAR, S., KOO, MS., KELLEY, C., AHMED, MF., CHAUDHURI, S. and
SARKAR, S., 2007. Conservation assessment and prioritization of areas in
Northeast India: Priorities for amphibians and reptiles. Biological
Conservation, vol. 136, no. 3, p. 346-361.
http://dx.doi.org/10.1016/j.biocon.2006.12.012.
http://dx.doi.org/10.1016/j.biocon.2006....
). The maximum entropy
algorithm assumes the less biased solution in the evaluation of an unknown
probability of distribution, maximising its entropy (Pawar et al., 2007PAWAR, S., KOO, MS., KELLEY, C., AHMED, MF., CHAUDHURI, S. and
SARKAR, S., 2007. Conservation assessment and prioritization of areas in
Northeast India: Priorities for amphibians and reptiles. Biological
Conservation, vol. 136, no. 3, p. 346-361.
http://dx.doi.org/10.1016/j.biocon.2006.12.012.
http://dx.doi.org/10.1016/j.biocon.2006....
). The algorithm differs from the others
(e.g., genetic algorithms, artificial neural networks) by building a
probability distribution, while the others produce a probability for each point
(see Pawar et al., 2007PAWAR, S., KOO, MS., KELLEY, C., AHMED, MF., CHAUDHURI, S. and
SARKAR, S., 2007. Conservation assessment and prioritization of areas in
Northeast India: Priorities for amphibians and reptiles. Biological
Conservation, vol. 136, no. 3, p. 346-361.
http://dx.doi.org/10.1016/j.biocon.2006.12.012.
http://dx.doi.org/10.1016/j.biocon.2006....
; Elith et al., 2011ELITH, J., PHILLIPS, S., HASTIE, T., DUDÍK, M., CHEE, YE. and
YATES, C., 2011. A statistical explanation of maxent for ecologists. Diversity
& Distributions, vol. 17, no. 1, p. 43-57.
http://dx.doi.org/10.1111/j.1472-4642.2010.00725.x.
http://dx.doi.org/10.1111/j.1472-4642.20...
). Despite its
sensibility to sampling bias, Maxent comparatively showed a better performance when
sampling involve poor number of points (Costa
et al., 2010COSTA, GC., NOGUEIRA, C., MACHADO, RB. and COLLI, GR., 2010.
Sampling bias and the use of ecological niche modelling in conservation
planning: a field evaluation in a biodiversity hotspot. Biodiversity and
Conservation, vol. 19, no. 3, p. 883-899.
http://dx.doi.org/10.1007/s10531-009-9746-8.
http://dx.doi.org/10.1007/s10531-009-974...
).
We used 20 bioclimatic variables provided by WorldClim. org (Hijmans et al., 2005HIJMANS, RJ., CAMERON, SE., PARRA, JL., JONES, PG. and JARVIS, A.,
2005. Very high resolution interpolated climate surfaces for global land areas.
International Journal of Climatology, vol. 25, no. 15, p. 1965-1978.
http://dx.doi.org/10.1002/joc.1276.
http://dx.doi.org/10.1002/joc.1276...
), with resolution of
30 arcseconds: annual mean temperature (bio 1); mean diurnal temperature
range (bio 2); isothermality (bio 3); temperature seasonality
(bio 4); maximum temperature of warmest month (bio 5); minimum
temperature of coldest month (bio 6); temperature annual range (bio
7); mean temperature of wettest quarter (bio 8); mean temperature of
driest quarter (bio 9); mean temperature of warmest quarter (bio
10); mean temperature of coldest quarter (bio 11); annual
precipitation (bio 12); precipitation of wettest month (bio 13);
precipitation of driest month (bio 14); precipitation seasonality
(bio 15); precipitation of wettest quarter (bio 16);
precipitation of driest quarter (bio 17); precipitation of warmest quarter
(bio 18); precipitation of coldest quarter (bio 19); and
altitude. Temperature data was expressed in °C, precipitation in mm and
altitude in meters above sea level - ASL. All matrices were than appraised in South
America range. To construct the model, we used the default threshold of convergence
(10–5) and the maximum number of data randomization
(500 iterations). For manipulation of matrices and the construction of
resulting maps, we used the software ArcGIS 9.3 (ESRI, 2008).
We performed a multivariate correlation test among occurrence points and
all environmental variables to avoid collinearity in the model, using R
(www.r-project.org). Variables with high correlation (up to
75%) were excluded from the analysis. The Jacknife test was performed to
evaluate which variables would be more important to the model regarding the amount
of information, from two processes: the first rebuilt the model adding one variable
at time; the second removes each variable. To validate and interpret the model,
usually it is preferable the distinction of suitable and non-suitable areas, though
the establishment of a cumulative threshold for the resulting model (presence
prediction) (Pearson et al.,
2007PEARSON, RG., RAXWORTHY, CJ., NAKAMURA, M. and TOWNSEND PETERSON,
AT., 2007. Predicting species distributions from small numbers of occurrence
records: a test case using cryptic geckos in Madagascar. Journal of
Biogeography, vol. 34, no. 1, p. 102-117.
http://dx.doi.org/10.1111/j.1365-2699.2006.01594.x.
http://dx.doi.org/10.1111/j.1365-2699.20...
). A threshold must be defined by the user (parameter
E), and consists into the amount of error associated to
the presence localities dataset (see Peterson
et al., 2008PETERSON, AT., PAPES, M. and SOBERON, J., 2008. Rethinking receiver
operating characteristics analysis applications in ecological niche modelling.
Ecological Modelling, vol. 213, no. 1, p. 63-72.
http://dx.doi.org/10.1016/j.ecolmodel.2007.11.008.
http://dx.doi.org/10.1016/j.ecolmodel.20...
). However, due to the reduced geographic distribution
and the well-studied populations (periodically monitored; see Rocha, 1992;
Rocha and Bergallo, 1992ROCHA, CFD. and BERGALLO, HG., 1992. Population decrease: the case
of Liolaemus lutzae, an endemic lizard of southeastern Brazil. Ciencia e
Cultura, vol. 49, no. 4, p. 269-274.; Rocha et al., 2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
, bROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009b. A potential
recovery of a population of the sand lizard Liolaemus lutzae Mertens, 1938 in an
area within its range: a lizard endemic and threatened with extinction.
Brazilian Journal of Biology, vol. 69, no. 1, p. 185-187.
http://dx.doi.org/10.1590/S1519-69842009000100024.
PMid:19347163
http://dx.doi.org/10.1590/S1519-69842009...
), we evaluated the resulting model based in the pattern
discrimination statistics (area under the curve on receiver operating
characteristics; AUC and ROC, respectively). These statistics provide the
evaluation that the model correctly classifies a point of actual presence and a
point of true absence (Phillips et al.
2006PHILLIPS, SJ., ANDERSON, RP. and SCHAPIRE, RE., 2006. Maximum
entropy modelling of species geographic distributions. Ecological Modelling,
vol. 190, no. 3-4, p. 231-259.
http://dx.doi.org/10.1016/j.ecolmodel.2005.03.026.
http://dx.doi.org/10.1016/j.ecolmodel.20...
). Values of AUC vary between zero and one, according to the
proportion of hits and errors: from zero to 0.5 the discrimination is at random, and
higher values indicate more hits (Elith et al.,
2006ELITH, J., GRAHAM, CH., ANDERSON, RP., DUDÍK, M., FERRIER, S.,
GUISAN, A., HIJMANS, RJ., HUETTMANN, F., LEATHWICK, JR., LEHMANN, A., LI, J.,
LOHMANN, LG., LOISELLE, BA., MANION, G., MORITZ, C., NAKAMURA, M., NAKAZAWA, Y.,
OVERTON, JM., PETERSON, AT., PHILLIPS, SJ., RICHARDSON, K., SCACHETTI-PEREIRA,
R., SCHAPIRE, RE., SOBERÓN, J., WILLIAMS, S., WISZ, MS. and ZIMMERMANN,
NE., 2006. Novel methods improve prediction of species' distributions from
occurrence data. Ecography, vol. 29, no. 2, p. 129-151.
http://dx.doi.org/10.1111/j.2006.0906-7590.04596.x.
http://dx.doi.org/10.1111/j.2006.0906-75...
). Values up to 0.75 are considered sufficiently discriminatory
(Elith et al., 2006ELITH, J., GRAHAM, CH., ANDERSON, RP., DUDÍK, M., FERRIER, S.,
GUISAN, A., HIJMANS, RJ., HUETTMANN, F., LEATHWICK, JR., LEHMANN, A., LI, J.,
LOHMANN, LG., LOISELLE, BA., MANION, G., MORITZ, C., NAKAMURA, M., NAKAZAWA, Y.,
OVERTON, JM., PETERSON, AT., PHILLIPS, SJ., RICHARDSON, K., SCACHETTI-PEREIRA,
R., SCHAPIRE, RE., SOBERÓN, J., WILLIAMS, S., WISZ, MS. and ZIMMERMANN,
NE., 2006. Novel methods improve prediction of species' distributions from
occurrence data. Ecography, vol. 29, no. 2, p. 129-151.
http://dx.doi.org/10.1111/j.2006.0906-7590.04596.x.
http://dx.doi.org/10.1111/j.2006.0906-75...
). To
evaluate the importance of each environmental variable used for modelling, we used
the Jacknife test, which estimates the amount of information retained by each
variable in its absence and in its presence (see García, 2006GARCÍA, A., 2006. Using ecological niche modelling to identify
diversity hotspots for the herpetofauna of Pacific lowlands and adjacent
interior valleys of Mexico. Biological Conservation, vol. 130, no. 1, p. 25-46.
http://dx.doi.org/10.1016/j.biocon.2005.11.030.
http://dx.doi.org/10.1016/j.biocon.2005....
).
3. Results
We considered 10 environmental variables in the final analysis, considered best applied among those non-correlated, for constructing the potential distribution model for Liolaemus lutzae: mean diurnal temperature range (bio 2); isothermality (bio 3); maximum temperature of warmest month (bio 5); mean temperature of driest quarter (bio 9); precipitation seasonality (bio 15); precipitation of wettest quarter (bio 16); precipitation of driest quarter (bio 17); precipitation of warmest quarter (bio 18); precipitation of coldest quarter (bio 19); and altitude.
The model identified as the most important environmental influencing the occurrence of L. lutzae the diurnal temperature range (41.1% of contribution), precipitation of coldest quarter (17.7%), altitude (17.2%), maximum temperature of warmest month (11.28%), isothermality (8.9%), and precipitation seasonality (3.1%). The estimation of the potential distribution suggested that this species was more likely to occur largely at sites located in coastal sand dune areas of Brazil (Figures 2-6). When compared to other species from L. wiegmannii group, the model for L. lutzae suggests a potential distribution in areas of currently occurrence of L. occipitalis, L. arambaraensis, L. wiegmanni, and L. multimaculatus (Figure 6). The model suggested altitude as the variable that provided the highest information gain when used in isolation, therefore holding the most useful information by itself. When the variables were removed one at a time, the diurnal temperature range decreased the gain when omitted, and therefore, retained the greatest amount of information that was absent in the other variables. AUC value for the model validation was considered satisfactory (AUCmodel = 0.974 ± 0.023).
Map of the resulting potential distribution of Liolaemus lutzae (Squamata, Liolaemidae) in Brazil.
Map of the resulting potential distribution of Liolaemus lutzae (Squamata, Liolaemidae) in the northern portion of known geographic distribution for the species (Rio de Janeiro, Espírito Santo and southern Bahia).
Resulting potential distribution of Liolaemus lutzae (Squamata, Liolaemidae) in the currently geographic distribution for the species (Rio de Janeiro). 1. Jurubatiba, 2. Grussaí.
Map of the resulting potential distribution of Liolaemus lutzae (Squamata, Liolaemidae) in the southeastern-southern portion of known geographic distribution for the species (São Paulo, Paraná, Santa Catarina and Rio Grande do Sul).
Distribution of the species of Liolaemus wiegmannii group. Modified from Avila et al. (2009)AVILA, LJ., MORANDO, M., PEREZ DR. and SITES JUNIOR, JW., 2009. A new species of Liolaemus from Añelo sand dunes, northern Patagonia, Neuquén, Argentina, and molecular phylogenetic relationships of the Liolaemus wiegmannii species group (Squamata, Iguania, Liolaemini). Zootaxa, vol. 2234, p. 39-55..
4. Discussion
The resulting potential distribution model of Liolaemus
lutzae differs in part from the known distribution of the species
(see Rocha et al., 2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
), with the
current distribution being a sub-set of the area to which the species could
potentially spread. Based on the model, we observe that there were sites with
potential for occurrence of populations in different areas on the coast of Brazil,
not only in the state of Rio de Janeiro. Probably, the environments in which the
species could potentially occur have similar general environmental characteristics.
Interestingly, validating the model, no inland area was predicted but instead, only
areas in coastal region. This coincide to the specialized ecological requirements of
the species, including thermal biology and psamophilic habits. In the state of Rio
de Janeiro, beyond the northern boundary of the known distribution of L.
lutzae, the areas with potential for occurrence of the model included
restinga habitats of Jurubatiba and Grussaí. However, outside the territory of
Rio de Janeiro state, both northwards and southwards, the probability of occurrence
decreased considerably due to the adjustment of the model to it's distribution
records. Besides those areas where the three cogeneric species live, the areas with
a potential for occurrence of L. lutzae indicated by the model
interestingly corresponded in part to the geographic distribution of the related
lizard Tropidurus torquatus (see Rodrigues, 1987RODRIGUES, MT., 1987. Sistemática, ecologia e Zoogeografia dos
Tropidurus do grupo torquatus ao Sul do Rio Amazonas (Sauria,
Iguanidae). Arquivos de Zoologia (São Paulo), vol. 31, no.
3, p. 105-230. http://dx.doi.org/10.11606/issn.2176-7793.
v31i3p105-230.
http://dx.doi.org/10.11606/issn.2176-779...
), including the restinga habitats of
Jurubatiba and Grussaí. In all areas of presence of L. lutzae,
T. torquatus occur sympatrically suggesting that they share
similar ecological requirements. Despite the Doce River region having been
recognised as an area of endemism, and potential barrier limiting taxa distributions
(see Rocha, 2000; Carnaval and Moritz,
2008CARNAVAL, AC. and MORITZ, C., 2008. Historical climate modelling
predicts patterns of current biodiversity in the Brazilian Atlantic forest.
Journal of Biogeography, vol. 35, no. 7, p. 1187-1201.
http://dx.doi.org/10.1111/j.1365-2699.2007.01870.x.
http://dx.doi.org/10.1111/j.1365-2699.20...
; Carnaval et al., 2009CARNAVAL, AC., HICKERSON, MJ., HADDAD, CF., RODRIGUES, MT. and
MORITZ, C., 2009. Stability predicts genetic diversity in the Brazilian Atlantic
forest hotspot. Science, vol. 323, no. 5915, p. 785-789.
http://dx.doi.org/10.1126/science.1166955.
PMid:19197066
http://dx.doi.org/10.1126/science.116695...
; Silva et al., 2012SILVA, SM., MORAES-BARROS, N., RIBAS, CC., FERRAND, N. and MORGANTE,
JS., 2012. Divide to conquer: a complex pattern of biodiversity depicted by
vertebrate components in the Brazilian Atlantic Forest. Biological Journal of
the Linnean Society, vol. 107, no. 1, p. 39-55.), T.
torquatus manages to overcome this area. For L.
lutzae, due to its limited occurrence to the narrow strip of beach areas
with herbaceous vegetation, it is possible that this species could never have been
able to overcome the Doce River. It is still unclear the reasons to why L.
lutzae does not occur in restinga areas northwards of its northern
known distribution in Rio de Janeiro state. Further detailed studies on
limiting/favouring variables affecting the occurrence of this species are still
needed, especially at the microhabitat and habitat scale, including sand granulation
and/or dominant winds. Areas with the greatest potential for occurrence in the
southern region (states of Santa Catarina and Rio Grande do Sul)
correspond to the distribution of its Brazilian congeners (L.
occipitalis and L. arambarensis), and could be
reflecting the historical distribution of a common ancestor with L.
arambarensis and L. occipitalis. The divergence
between L. lutzae and L. occipitalis was
previously estimated at about 4,000 years ago (Vanzolini and Ab'Saber, 1968VANZOLINI, PE. and AB'SABER, AN., 1968. Divergence rate in
South American lizards of the genus Liolaemus (Sauria, Iguanidae).
Papéis Avulsos de Zoologia, vol. 21, p. 205-208.); however, the divergence among
L. arambaraensis and the other two species has not yet been
estimated. Suitable areas in southern São Paulo for the occupation by the
species Liolaemus has been already hypothesised (Vanzolini and Ab'Saber, 1968VANZOLINI, PE. and AB'SABER, AN., 1968. Divergence rate in
South American lizards of the genus Liolaemus (Sauria, Iguanidae).
Papéis Avulsos de Zoologia, vol. 21, p. 205-208.), and
these areas were also indicated by our model. Species from L.
wiegmannii group are currently well distributed in subtropical zone,
mainly between 24°S and 40°S (see Avila et al., 2009AVILA, LJ., MORANDO, M., PEREZ DR. and SITES JUNIOR, JW., 2009. A
new species of Liolaemus from Añelo sand dunes, northern Patagonia,
Neuquén, Argentina, and molecular phylogenetic relationships of the
Liolaemus wiegmannii species group (Squamata, Iguania, Liolaemini).
Zootaxa, vol. 2234, p. 39-55.). The east portion of this area (which
includes Brazilian territory) suffered markedly modifications on topography and
climate along time, as well as intense tectonic activity (Ribeiro, 2006RIBEIRO, AC. 2006. Tectonic history and the biogeography of the
freshwater fishes from the coastal drainages of eastern Brazil: an example of
faunal evolution associated with a divergent continental margin. Neotropical
Ichthyoogy, vol. 4, no. 2, p. 225-246.). Together with the Antartic cold front
during late Quaternary, the region of the currently southern portion of the Atlantic
Rainforest was not suitable for a forest on various periods (see Behling, 2002BEHLING, H., 2002. South and southeast Brazilian grasslands during
late Quaternary times: a synthesis. Palaeogeography, Palaeoclimatology,
Palaeoecology, vol. 177, no. 1-2, p. 19-27.
http://dx.doi.org/10.1016/S0031-0182(01)00349-2.
http://dx.doi.org/10.1016/S0031-0182(01)...
). The opened-vegetation
habitat in subtropical region of South America may have provided the appropriate
habitat for species diversification of L. wiegmannii group. On the
southeastern and southern coast of Brazil, where the species of
Liolaemus currently occur, the 24°S corresponds to middle
Paraná region, previously recognised as an endemism area (see Cracraft, 1985CRACRAFT, J., 1985. Historical biogeography and patterns of
differentiation within the South American avifauna: areas of endemism.
Ornithological Monographs, vol. 1985, no. 36, p. 49-84.
http://dx.doi.org/10.2307/40168278.
http://dx.doi.org/10.2307/40168278...
; Silva et al., 2012SILVA, SM., MORAES-BARROS, N., RIBAS, CC., FERRAND, N. and MORGANTE,
JS., 2012. Divide to conquer: a complex pattern of biodiversity depicted by
vertebrate components in the Brazilian Atlantic Forest. Biological Journal of
the Linnean Society, vol. 107, no. 1, p. 39-55.). Based on vegetation modelling using
palynological data, Carnaval and Moritz
(2008)CARNAVAL, AC. and MORITZ, C., 2008. Historical climate modelling
predicts patterns of current biodiversity in the Brazilian Atlantic forest.
Journal of Biogeography, vol. 35, no. 7, p. 1187-1201.
http://dx.doi.org/10.1111/j.1365-2699.2007.01870.x.
http://dx.doi.org/10.1111/j.1365-2699.20...
predicted the occurrence of forest in a longitudinal
stripe, which could act as a barrier for organisms restricted to more opened
environments. Liolaemus lutzae is the only species from this group
that exceeds this latitudinal constraint (see Avila et al., 2009AVILA, LJ., MORANDO, M., PEREZ DR. and SITES JUNIOR, JW., 2009. A
new species of Liolaemus from Añelo sand dunes, northern Patagonia,
Neuquén, Argentina, and molecular phylogenetic relationships of the
Liolaemus wiegmannii species group (Squamata, Iguania, Liolaemini).
Zootaxa, vol. 2234, p. 39-55.), suggesting a probably more ancient
distribution from the south to at least up to the Doce River, potentially
benefitting from a more open vegetation and from transgressions and regressions of
the sea, followed by differentiation due to vicariance. Consequently, the divergence
between L. lutzae and L. occipitalis may
potentially be more ancient than the hypothesis of Vanzolini and Ab'Saber (1968)VANZOLINI, PE. and AB'SABER, AN., 1968. Divergence rate in
South American lizards of the genus Liolaemus (Sauria, Iguanidae).
Papéis Avulsos de Zoologia, vol. 21, p. 205-208..
The area with the greatest values for probability of occurrence of
L. lutzae is adjusted to the currently known geographic
distribution. This result was expected, since the maximum entropy algorithm is
biased by concentration of points (see Costa et
al., 2010COSTA, GC., NOGUEIRA, C., MACHADO, RB. and COLLI, GR., 2010.
Sampling bias and the use of ecological niche modelling in conservation
planning: a field evaluation in a biodiversity hotspot. Biodiversity and
Conservation, vol. 19, no. 3, p. 883-899.
http://dx.doi.org/10.1007/s10531-009-9746-8.
http://dx.doi.org/10.1007/s10531-009-974...
). However, the model validation values were considered
satisfactory. Further validation to the model can be provided by the low prediction
value to the location of Praia das Neves as an presently area of occurrence of the
species, since there was no previous occurrence of native populations previously to
the deliberate intentional introduction of a set of individuals in the area which
established a successfully population (see Soares and Araújo, 2008SOARES, AHB. and ARAÚJO, AFB., 2008. Experimental introduction
of Liolaemus lutzae (Squamata, Iguanidae) in Praia das Neves, State of
Espírito Santo, Brazil: a descriptive study 18 years later. Revista
Brasileira de Zoologia, vol. 25, no. 4, p. 640-646.
http://dx.doi.org/10.1590/S0101-81752008000400009.
http://dx.doi.org/10.1590/S0101-81752008...
). Apparently, the large Paraiba do Sul
River, which divides the states of Rio de Janeiro and Espírito Santo, may
constitute an effective geographic barrier, although currently the northern known
population of L. lutzae is located in Cabo Frio (Rocha, 1986ROCHA, CFD., 1986. Distribuição geográfica de
Liolaemus lutzae (Sauria: Iguanidae) um lagarto endêmico do
estado do Rio de Janeiro. Boletim Fundação Brasileira Para a
Conservação da Natureza, vol. 21, no. 1, p. 163-167.; Rocha et al., 2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
).
The potential distribution of L. lutzae suggests that its occurrence would be possible in other areas of the Brazilian coast, considering the variables selected for this study. However, these areas are mainly located at southern coastal areas of Brazil. It is likely that L. lutzae had a greater geographic distribution historically, and that its extensive former coastal distribution area successively decreased, partly due to the sea transgressions which have suppressed extensive areas of restinga habitats along the states of Paraná, São Paulo and in the southern portion of Rio de Janeiro state.
Also, it is important to consider the human actions which can have
started even during pre-colonisation and that have intensively increased throughout
the last five centuries since colonisation. The elimination of some current
populations (see Rocha and Bergallo,
1992ROCHA, CFD. and BERGALLO, HG., 1992. Population decrease: the case
of Liolaemus lutzae, an endemic lizard of southeastern Brazil. Ciencia e
Cultura, vol. 49, no. 4, p. 269-274.; Rocha et al., 2009aROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009a. The endemic and
threatened Liolaemus lutzae (Squamata: Liolaemidae): current
geographic distribution and areas of occurrence with estimated population
densities. Zoologia (Curitiba), vol. 26, no. 3, p. 454-460.
http://dx.doi.org/10.1590/S1984-46702009000300009.
http://dx.doi.org/10.1590/S1984-46702009...
)
and the potential recovery when correct policies for the conservation of the species
(Rocha et al., 2009bROCHA, CFD., SIQUEIRA, CC. and ARIANI, CV., 2009b. A potential
recovery of a population of the sand lizard Liolaemus lutzae Mertens, 1938 in an
area within its range: a lizard endemic and threatened with extinction.
Brazilian Journal of Biology, vol. 69, no. 1, p. 185-187.
http://dx.doi.org/10.1590/S1519-69842009000100024.
PMid:19347163
http://dx.doi.org/10.1590/S1519-69842009...
) are
adopted suggest this. Alternatively, it may suggest a larger geographic distribution
of an ancient lineage of the genus Liolaemus on the coast of
Brazil. The conservation of restinga environments in the state of Rio de Janeiro is
imperative, for the maintenance of the species, now restricted to small areas that
strongly suffer ongoing human impacts.
Acknowledgements – We are grateful to the “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq), which provided the Postdoctoral Research Fellowship grant to GRW (process n° 150855/2012-5), and financial support to CFDR (processes n° 304791/2010-5 and n° 470265/2010-8). We also thank the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) for the support to CFDR through “Programa Cientistas do Nosso Estado” (process number E-26/102.404/2009), and pos doctoral fellowship Grant at present to GRW (processnumber E-26/101.237/2013). PAS receives a scholarship fund from the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior” (CAPES). We appreciate the logistic support provided by Alberto Senra Gonçalves and Carolina Pietczak, and contributions made by Daniel Passos and the anonymous referee.
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Publication Dates
-
Publication in this collection
May 2014
History
-
Received
27 Aug 2012 -
Accepted
18 Mar 2013