Acta Herpetologica 14(2): 147-151, 2019 ISSN 1827-9635 (print) © Firenze University Press ISSN 1827-9643 (online) www.fupress.com/ah DOI: 10.13128/a_h-7754 Estimating abundance of the Stripeless tree-frog Hyla meridionalis by means of replicated call counts Federico Crovetto, Sebastiano Salvidio, Andrea Costa* DISTAV, University of Genova, Corso Europa 26, I-16132 Genova, Italy. *Corresponding author. Email: andrea-costa-@hotmail.it Submitted on: 2019, 16th May; revised on: 2019, 22nd July; accepted on: 2019, 16th September Editor: Marco Mangiacotti Abstract. The Stripeless tree-frog Hyla meridionalis reaches its eastern-most European distributional limit in NW Ita- ly, and specifically in the Cinque Terre National Park. Here for two consecutive years, we estimated tree-frog popula- tion abundance by call surveys at 24 sites. Data were analysed in the framework of N-mixture open population mod- els based on repeated counts of calling males. The results obtained by this statistical approach were effective in esti- mating population size together with annual recruitment and survival. The tree-frog male population size remained constant between years and site abundance was inversely related with altitude. On the bases of these findings, our application of N-mixture models to tree-frog calling males was successful and is a promising cost-effective method to obtain long-term monitoring data on this species over large geographic areas. Keywords. Abundance estimation, call surveys, Cinque Terre National Park, detection probability, N-mixture models. The Stripeless tree-frog Hyla meridionalis Boettger, 1874 is found in North-western Africa (Algeria, Morocco and Tunisa), South-western Europe (Portugal, Spain, South France and North-Western Italy) and the Canary and Balearic Islands (Sillero, 2010). On a portion of its distribu- tion range the species is considered introduced, i.e., Canary and Balearic Islands (Sillero et al., 2014), and it is also pos- sibly introduced for other European regions (Recuero et al., 2007). In Italy, the Stripeless tree-frog is common along the Mediterranean coast of Liguria (NW Italy), from the Province of Imperia to the province of La Spezia (Salvidio, 2007). Apart from morphometric and distributional data (Salvidio, 2007), little is known about the abundance and dynamics of Stripeless tree-frog populations in Italy, and quantitative data on populations size should be obtained to assess the species status and its ecological requirements, in particular near the species distribution limits, where a high population fragmentation is expected (Gaston, 2003). Although photo-identification of Stripeless tree-frogs is possible (Crovetto unpublished data), the animals are arboreal and highly secretive during daytime. The use of PVC pipes may increase the probability of detection of tree-frogs (do Vale et al., 2018), however, in the CTNP the majority of the species’ reproductive habitats are on private lands, and thus are not freely accessible (Romano et al., 2014). Therefore, the monitoring technique select- ed to estimate population size was based on nocturnal auditory surveys of calling males, because of the species highly distinctive mating call (Schneider, 1974; Márquez et al., 2005). Call survey is a relatively efficient technique for evaluating the distribution and diversity of anurans (Dorcas et al., 2009). Therefore, calling surveys are fre- quently used in large-scale amphibian monitoring pro- grammes (e.g., Anthony, 2002; Weir and Mossman, 2005; Weir et al., 2005, 2009). However, the use of call surveys for estimating population abundances and trends suffers of the same problematic issues recognized in the case of repeated counts of individuals, because the detectability of anuran calling males is < 1 (i.e., not all males are call- ing in the same night; Schmidt and Pellet, 2005. Moreo- 148 Federico Crovetto, Sebastiano Salvidio, Andrea Costa ver, anuran mating call activities display high variation in response to biotic and abiotic factors, that usually remain unknown and difficult to model (Royle and Link, 2005; Droege and Eagle, 2009). In fact, using raw counts of calling males or even scores derived from abundance indexes (i.e., indexes that group calling males by classes of relative abundance; Weir and Mossman, 2005) without accounting for detection probability may lead to relevant bias in abundance and trend estimates (Schmidt, 2004; Mazerolle et al., 2007). Therefore, to reliably estimate population abundance, the information derived from raw counts of calling males should always be corrected for species-specific detection probabilities (Schmidt and Pellet, 2005; Royle and Link, 2005). Recently, specific modelling approaches have been proposed for estimating anuran population abundances from the count of anuran calling indexes taking into account detection probabilities (Royle, 2004a; Royle and Link, 2005). This study aimed to estimate the abundance of Stripeless tree-frog males together with some demo- graphic parameters and ecological requirements in Italy, at the eastern limit of the species distribution. Moreover, we tried to establish a cost-effective monitoring protocol to provide future population trends. Because of the rela- tively small number of tree-frog males recorded per site, we had the opportunity to apply the open population generalization of Royle’s (2004b) N-mixture model (Dail and Madsen, 2011) to count data derived from call sur- veys. The eastern-most limit of the species’ range in Europe is the village of Riomaggiore (Province of La Spezia), in the Cinque Terre National Park (CTNP), a protected area where the Stripeless tree-frog reproduces in streams and in artificial water tanks used for irriga- tion (Salvidio, 2007; Romano et al., 2014). In this area water streams display short and steep courses, with rela- tively long summer drying periods, due to the lack of precipitations (Olivari et al., 2013). Among many pos- sible land use of rural areas, agriculture is the only one in the CTNP, and from the sea level up to the hill tops vineyards and orchards are cultivated on strips of arable land, or “terraces”, sustained by dry-stone walls. Irriga- tion is provided by means of water stored in tanks, often colonised by amphibians (Olivari et al., 2013; Romano et al., 2014). The survey sites were selected during both day- time and nocturnal preliminary surveys. During the day, streams and water reservoirs were inspected and selected as potential reproductive sites if adults, larvae or eggs of some amphibian species were observed. During the night, sites were located by perceiving the calls of Stripeless three-frog males. In total 24 sites were surveyed in the municipalities of Levanto, Monterosso and Riomaggiore (from West to East): 6 streams and 18 artificial water res- ervoirs in agricultural lands or urban settings (Table 1; Fig. S1). All surveys began after sundown and after hear- ing the first tree-frog calls. In 2017 three nocturnal sur- veys were performed, from the end of March to May, by two operators that counted the number of males calling at each site during a two minute period. In 2018, three nocturnal surveys were performed, from the beginning of May to the beginning of June, with the same observers and procedure of 2017. In addition in 2018, a fourth sur- vey was performed by a single operator that tallied call- ing males for 4 minutes. The asynchrony and the different tonalities of calls permitted to count with confidence the minimum number of males per site that, in all cases, was ≤ 6 (Table S1). All sites were surveyed during the same overcast or rainy night, but never during heavy showers that could hinder a clear hearing of frog calls. Four cli- matic variables were obtained from the meteorological station of Levanto: rainfall during the 24 h preceding the survey (RAIN), air temperature (TEMP), relative humid- ity (RH) and wind speed (WIND), recorded during the last hour of survey. These weather variables were select- ed, because they are known to influence anuran calling behaviour (e.g., Walls et al., 2011). Finally, for each site three variables were considered: altitude above the sea level (ELEV), a categorical variable for the municipality of the site (CITY) and if the water body was a stream or an artificial site (SITE). Repeated count data were analysed using the Dail- Madsen (2011) model, which is a generalization of the Royle’s (2004b) N-mixture model, capable of relaxing the closure assumption by considering the population closed to immigration/births and emigration/deaths during a short period (i.e., three/four survey nights performed each year), while considering the population demograph- ically open between years, in a robust design-similar approach. This model estimates four parameters, two of which are in common with the Royle’s (2004b) N-mix- ture original formulation: individual detection prob- Table 1. Continuous variables included in the N-mixture open population models (Dail and Madsen, 2011) used to estimate Hyla meridionalis abundance, in the Cinque Terre National Park. Variable Description Sample size (N) Mean (SD) min max ELEV Site altitude (m) - 24 35.04 (29.66) 8 83 TEMP Air temperature (°C) 7 17.14 (3.44) 12 21 WIND Wind speed (m/s) 7 3.57 (1.27) 2 5 RH Relative humidity (%) 7 66 (13.55) 45 85 149N-mixture models for Hyla meridionalis call counts ability (p) and mean initial abundance for each site (λ). The Dail-Madsen (2011) model estimates two additional parameters: the recruitment rate (γ), comprehensive of births and immigrations, and the apparent survival prob- ability (ω), comprehensive of deaths and emigrations. In our study, we built models with Poisson error distribu- tion, since Negative Binomial distribution could lead to identifiability issues and may produce infinite abundance estimates (Barker et al., 2017; Link et al., 2018). Further- more, in order to avoid truncated estimates of abundance (Knape et al., 2018), we set the upper limit for integration (K) to 50 (i.e., we checked estimate stability at incremen- tal values of K). We then began the model building pro- cedure by fitting a global model (i.e., the most complex model on which other models are nested) and assessing the fit of this model in two ways: i) by means of a Pear- son chi-square test (MacKenzie and Bailey, 2004), using a parametric bootstrap procedure (5000 re-samplings), ii) by inspecting residuals (Knape et al., 2018). In order to avoid overfitting and creating too many models, deriv- ing from the combinations of covariates for each of the four parameters of the Dail-Madsen (2011) model, which can lead to uninformative and biologically unsound models, we preferred to build fewer models in a step- wise approach, considering one parameter at a time, and building biologically informative models. We proceeded modelling the detection probabil- ity, considering it to be constant, time-dependent, or to be affected by climatic variables. Then we modelled the initial abundance as a function of site specific covariates (ELEV, CITY and SITE) or constant over sites. Finally, we considered the survival to be influenced by the same site covariates as abundance, or constant over sites. For each model we considered recruitment as constant. We ranked all models with Akaike’s Informative Criterion corrected for small samples (AICc). We conducted model selection and considered only models with ∆AICc > 2 (Burnham and Anderson, 2002). Modelling was conducted in the R environment with package Unmarked (Fiske and Chan- dler, 2011) and AICcmodavg (Mazerolle, 2017). In 2017 we counted a total of 131 male frog calls during three surveys (44; 45; 42; respectively), while in 2018 we counted 129 male frogs during four surveys (33; 37; 32; 27; respectively, Table S1). The global model had a good fit (goodness-of-fit, P = 0.34; c-hat overdispersion = 1.12, and visual inspection of residuals). Model building procedure produced a total of 15 models (Table 2). The most supported model included elevation as a covariate on the initial abundance, highlighting a negative effect of elevation (βELEV = -0.331; 95% CI = -0.59 to -0.08; Figure 1). The estimated mean frog abundance per site was 3.4 (95% CI = 2.5 – 4.6). Individual detection probability for this model was constant, and estimated as p = 0.53 (95% CI = 0.42 – 0.63). Survival probability between years was considered constant among sites and resulted ω = 0.71 (95% CI = 0.50 – 0.86). Finally, the recruitment rate was constant across sites γ = 0.11 (95% CI = 0.00 – 12.10). From this best model we also obtained, as a derived parameter from the posterior distribution of the latent abundance, the total abundance of surveyed sites, which Table 2. Candidate N-mixture open population models (Dail and Madsen, 2011) used to estimate Hyla meridionalis abundance, ranked by AICc. γ = recruitment rate; λ= initial site abundance; p = individual detection probability; ω = survival; AICcWT = model weights. In model list t stands for time dependence. For covariate abbreviations see table 1. Model Parameters AICc ∆AICc AICcWT λ(ELEV) p(.) ω(.) γ(.) 5 451.35 0.00 0.40 λ(ELEV) p(.) ω(ELEV) γ(.) 6 453.45 2.1 0.14 λ(CITY) p(.) ω(.) γ(.) 6 453.98 2.63 0.11 λ(ELEV) p(.) ω(CITY) γ(.) 6 454.95 3.61 0.07 λ(.) p(.) ω(.) γ(.) 4 454.96 3.61 0.07 λ(SITE) p(.) ω(.) γ(.) 5 455.05 3.70 0.06 λ(ELEV) p(.) ω(SITE) γ(.) 7 456.15 4.80 0.04 λ(.) p(TEMP) ω(.) γ(.) 5 456.81 5.46 0.03 λ(.) p(.) ω(SITE) γ(.) 5 456.93 5.59 0.02 λ(.) p(RH) ω(.) γ(.) 5 457.14 5.80 0.02 λ(.) p(WIND) ω(.) γ(.) 5 475.54 6.20 0.02 λ(.) p(RAIN) ω(.) γ(.) 5 458.03 6.69 0.01 λ(.) p(.) ω(ELEV) γ(.) 5 459.15 7.80 0.01 λ(.) p(.) ω(CITY) γ(.) 6 459.35 8.01 0.01 λ(.) p(t) ω(.) γ(.) 11 483.07 31.73 0.00 Fig. 1. Effect of elevation on site specific abundance of Hyla meridi- onalis in the Cinque Terre National Park, with 95% confidence intervals, obtained by N-mixture open population modelling (Dail and Madsen, 2011). 150 Federico Crovetto, Sebastiano Salvidio, Andrea Costa resulted of 89 frogs in 2017 (95% CI = 81 – 147) and 64 frogs in 2018 (95% CI = 60 – 109). Our study showed that N-mixture modelling applied to individual frog calls can be successfully used to esti- mate male population size together with demographic parameters and ecological understandings. In the CTNP, where H. meridionalis is a species of high conservation concern, the male tree-frog population size showed no significant change between years, and site abundance was negatively related with altitude (Salvidio, 2007; Sillero, 2010). Moreover, the usefulness of N-mixture approach may be appreciated by comparing population estimated corrected by detectability to raw counts that, in the pre- sent case, underestimated the total number of males by about 45%, in both years. Another important application of N-mixture population open models (Dail and Mad- sen, 2011) relies on the possibility of estimating temporal variations in inter-annual population size, this informa- tion being of interest in conservation and management programmes concerning protected species characterised by low or variable detection probabilities (Ficetola et al., 2018). Conversely, the major limits of our study were that the occurrence of calling males does not always assure for the presence of a breeding site, while no data on popula- tion structure (i.e., population sex ratio and proportion of juveniles) can be provided (Dorcas et al., 2009). In any case, N-mixture models are cost-effective alternatives to mark-recapture and removal sampling methods (Kéry and Royle, 2015; Kéry, 2018), and they have been used to esti- mate population size and temporal trends of many spe- cies in very different ecological contexts (e.g., Priol et al., 2014; Romano et al., 2017; Kéry, 2018; Costa et al., 2019). However, to our knowledge there are few applications of N-mixture modelling to anuran call counts, because of the difficulties in correctly counting calling males in large frog choruses when dozens of calls are synchronous (Weir and Mossman, 2005). Nevertheless, when few individual males are calling at each site the application of the N-mixture modelling seems useful and can be preferred to other methods that estimate population abundance because there is no need to mark and recapture the focal individu- als (Royle, 2004a; Royle and Link, 2005). ACKNOWLEDGMENTS The constructive comments of two anonymous reviewers on a previous draft of the manuscript are appreciated. This research was funded by the Cinque Terre National Park within the programme “Azione di Sistema - Monitoraggio delle specie di habitat umidi- acquatici”. SUPPLEMENTARY MATERIAL Supplementary material associated with this article can be found at manuscript number 25342. REFERENCES Anthony, B.P. (2002): Results of the first batrachian sur- vey in Europe using road call counts. Alytes 20: 55-66. Barker, R.J., Schofield M.R., Link W.A., Sauer, J.R. (2017): On the reliability of N-mixture models for count data. Biometrics 74: 369-377. Burnham, K.P., Anderson, D.R. (2002): Model selection and multimodel inference: a practical information- theoretic approach. Springer-Verlag, New York, USA. Costa, A., Oneto, F., Salvidio,S. (2019): Time-for-space substitution in N-mixture modelling and population monitoring. J. Wildl. Manage. 83:737-741. Dail, D., Madsen, L. (2011): Models for Estimating Abun- dance from Repeated Counts of an Open Metapopu- lation. Biometrics 67: 577-587. Dorcas, M.E., Price, S.J., Walls, S.C., Barichivich, W.J. (2009): Auditory monitoring of anuran populations. In: Ecology and conservation of amphibians: a hand- book of techniques, pp. 281-298. Dodd, C.K. Jr., Ed., Oxford University Press, New York, USA. do Vale, R.L., Torres, I., Gomes, S., Fonseca, C., Ferrei- ra, E. (2018): Ecological preferences of Hyla molleri in the colonisation of arboreal refuges in a human- shaped wetland. Amphibia-Reptilia 39: 51-61. Droege, S., Eagle, P. (2009): Evaluating calling surveys. In: Declining amphibians: A United States response to the global phenomenon, pp. 314-325. Lannoo, M.J., Ed., University of California Press, Berkeley, Califor- nia, USA. Ficetola F., Salvidio S., Romano A., Sindaco R. (2018): Optimizing monitoring schemes to detect trends in abundance over broad scales. Anim. Cons. 21: 221- 231. Fiske, I., Chandler, R., (2011): Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43: 1-23. Gaston, K.J. (2003): The structure and dynamics of geo- graphic ranges. Oxford University Press, Oxford, UK. Kéry, M. (2018): Identifiability in N-mixture models: a large-scale screening test with bird data. Ecology 99: 281-288. Kéry, M., Royle, J.A. (2015): Applied hierarchical mod- eling in ecology - modeling distribution, abundance and species richness using R and BUGS. Volume 1: 151N-mixture models for Hyla meridionalis call counts Prelude and static models. Elsevier/Academic Press, Amsterdam, The Netherlands. Knape, J., Arlt, D., Barraquand, F., Berg, Å., Chevalier, M., Pärt, T., Ruete, A., Żmihorski, M. (2018): Sensitiv- ity of binomial N-mixture models to overdispersion: the importance of assessing model fit. Met. Ecol. Evol. 9: 2102-2114. Link, W.A., Schofield, M.R., Barker, R.J., Sauer, J.R. (2018): On the robustness of N-mixture models. Ecol- ogy 99: 1547-1551. MacKenzie, D.I., Bailey, L.L. (2004): Assessing the fit of site-occupancy models. J. Agric. Biol. Environ. Stat. 9: 300-318. Márquez, R., Moreira, C., do Amaral, J.P.S., Pargana, J.M., Crespo, E.G. (2005): Sound pressure level of adver- tisement calls of Hyla meridionalis and Hyla arborea. Amphibia-Reptilia 26: 391-395. Mazerolle, M.J., Bailey, L.L., Kendall, W.L., Royle, J.A., Converse, S.J., Nichols, J.D. (2007): Making great leaps forward: accounting for detectability in herpetological field studies. J. Herpetol. 41:672-689. Mazerolle, M.J. (2017): AICcmodavg: model selec- tion and multimodel inference based on (Q)AIC(c). R package version 2.1-1. https://cran.r-project.org/ package=AICcmodavg. [Accessed on 22 January 2019] Olivari, S., Romano, A., Salvidio, S. (2013): Anfibi e habi- tat acquatici nel Parco Nazionale delle Cinque Terre - Censimento e indirizzi per la conservazione. Edizioni Belvedere, Latina. Priol, P., Mazerolle, M., Imbeau, L., Drapeau, P., Trudeau, C., Ramière, J. (2014): Using dynamic N-mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation. Ecol. Evol. 4: 2165-2177. Recuero, E., Iraola, A., Rubio, X., Machordom, A., García-París, M. (2007): Mitochondrial differentiation and biogeography of Hyla meridionalis (Anura: Hyli- dae): An unusual phylogeographical pattern. J. Bioge- ogr. 34: 1207-1219. Romano, A., Costa, A. Basile, M. Raimondi, R., Posillico, M., Scinti Roger, D., Crisci, A., Piraccini, R., Raia, P., Matteucci, G., De Cinti, B. (2017): Conservation of salamanders in managed forests: Methods and costs of monitoring abundance and habitat selection. For. Ecol. Manage. 400: 12-18. Romano, A., Salvidio, S., Mongillo, D., Olivari, S. (2014): Importance of a traditional irrigation system in amphibian conservation in the Cinque Terre National Park (NW Italy). J. Nat. Cons. 22: 445-452. Royle, A.J. (2004a): Modeling abundance index data from anuran calling surveys. Cons. Biol. 18: 1378-1385. Royle, A.J. (2004b): N-mixture models for estimat- ing population size from spatially replicated counts. Biometrics 60: 108-115. Royle, J.A., Link; W.A (2005): A general class of multino- mial mixture models for anuran calling survey data. Ecology 86: 2505-2512. Salvidio, S. (2007): Hyla meridionalis Boettger, 1874. In: Fauna d’Italia – Amphibia. XLII, pp. 338-346. Lanza, B., Andreone, F., Bologna, M.A., Corti, C., Razzetti, E., Eds, Calderini, Bologna. Schmidt, B.R., (2004): Declining amphibian populations: the pitfalls of count data in the study of diversity, dis- tributions, dynamics, and demography. Herpetol. J. 14: 167-174. Schmidt, B.R., Pellet, J. (2005): Relative importance of population processes and habitat characteristics in determining site occupancy of two anurans. J. Wildlife Manage. 69: 884-893. Schneider, H. (1974): Structure of the mating calls and relationships of the European tree frogs (Hylidae, Anura). Oecologia 14: 99-110. Sillero, N. (2010): Modelling suitable areas for Hyla meridionalis under current and future hypothetical expansion scenarios. Amphibia-Reptilia 31: 37-50. Sillero, N., Campos, J., Bonardi, A., Corti, C., Creem- ers, R., Crochet, P-A., Crnobrnja Isailovic, I., Denoël, M., Ficetola, G.F., Gonçalves, J., Kuzmin, S., Lym- berakis, P., de Pous, P., Rodríguez, A., Sindaco, R., Speybroeck,J., Toxopeus, B., Vieites, D.R., Vences, M. (2014): Updated distribution and biogeography of amphibians and reptiles of Europe. Amphibia-Reptilia 35: 1-31. Walls, S., Waddle, J.H., Dorazio, R.M. (2011): Estimating occupancy dynamics in an anuran assemblage from Louisiana, USA. J. Wildlife Manage. 75: 751-761. Weir, L., Fiske, I.J., Royle, J.A. (2009): Trends in anuran occupancy from northeastern states of the North American Amphibian Monitoring Program. Herpetol. Cons. Biol. 4: 389-402. Weir, L.A., Mossman M.J. (2005): North American Amphibian Monitoring Program. In: Declining amphibians: A United States response to the global phenomenon, pp. 307-313. Lannoo, M.J., Ed, Univer- sity of California Press, Berkeley, California, USA. Weir, L.A., Royle, J.A., Nanjappa, P., Jung, R.E. (2005): Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland. J. Herpetol. 39: 627- 639. Acta Herpetologica Vol. 14, n. 2 - December 2019 Firenze University Press Podarcis siculus latastei (Bedriaga, 1879) of the Western Pontine Islands (Italy) raised to the species rank, and a brief taxonomic overview of Podarcis lizards Gabriele Senczuk1,2,*, Riccardo Castiglia2, Wolfgang Böhme3, Claudia Corti1 Substrate type has a limited impact on the sprint performance of a Mediterranean lizard Pantelis Savvides1,*, Eleni Georgiou1, Panayiotis Pafilis2,3, Spyros Sfenthourakis1 Coping with aliens: how a native gecko manages to persist on Mediterranean islands despite the Black rat? Michel-Jean Delaugerre1,*, Roberto Sacchi2, Marta Biaggini3, Pietro Lo Cascio4, Ridha Ouni5, Claudia Corti 3 PIT-Tags as a technique for marking fossorial reptiles: insights from a long-term field study of the amphisbaenian Trogonophis wiegmanni Pablo Recio, Gonzalo Rodríguez-Ruiz, Jesús Ortega, José Martín* Occurrence of Batrachochytrium dendrobatidis in the Tensift region, with comments on its spreading in Morocco Ait El Cadi Redouane1, Laghzaoui El-Mustapha1, Angelica Crottini2, Slimani Tahar1, Bosch Jaime3,4, EL Mouden El Hassan1,* Hematological parameters of the Bolson tortoise Gopherus flavomarginatus in Mexico Cristina García-De la Peña1,*, Roger Iván Rodríguez-Vivas2, Jorge A. Zegbe-Domínguez3, Luis Manuel Valenzuela-Núñez1, César A. Meza Herrera4, Quetzaly Siller-Rodríguez1, Verónica Ávila-Rodríguez1 Ontogenetic and interspecific variation in skull morphology of two closely related species of toad, Bufo bufo and B. spinosus (Anura: Bufonidae) Giovanni Sanna Visible Implant Alphanumeric (VIA) as a marking method in the lesser snouted treefrog Scinax nasicus Andrea Caballero-Gini1,2,3,*, Diego Bueno Villafañe2,3, Lía Romero2, Marcela Ferreira2,3, Lucas Cañete4, Rafaela Laino2, Karim Musalem2,5 Morphological variation of the newly confirmed population of the Javelin sand boa, Eryx jaculus (Linnaeus, 1758) (Serpentes, Erycidae) in Sicily, Italy Francesco P. Faraone1,*, Salvatore Russotto2, Salvatore A. Barra3, Roberto Chiara3, Gabriele Giacalone4, Mario Lo Valvo3 Variability in the dorsal pattern of the Sardinian grass snake (Natrix natrix cetti) with notes on its ecology Enrico Lunghi1,2,3,4,*, Simone Giachello5, Manuela Mulargia6, Pier Paolo Dore7, Roberto Cogoni8, Claudia Corti1 Estimating abundance of the Stripeless tree-frog Hyla meridionalis by means of replicated call counts Federico Crovetto, Sebastiano Salvidio, Andrea Costa* AT-rich microsatellite loci development for Fejervarya multistriata by Illumina HiSeq sequencing Yan-Mei Wang, Jing-Yi Chen, Guo-Hua Ding*, Zhi-Hua Lin