Combined effect of ENSO and SAM on the population dynamics of the invasive yellowjacket wasp in central Chile

Loading.... (view fulltext now)

Loading....

Loading....

Loading....

Loading....

Texto completo

(1)Popul Ecol (2010) 52:289–294 DOI 10.1007/s10144-009-0179-8. ORIGINAL ARTICLE. Combined effect of ENSO and SAM on the population dynamics of the invasive yellowjacket wasp in central Chile Sergio A. Estay • Mauricio Lima. Received: 5 May 2009 / Accepted: 23 August 2009 / Published online: 7 October 2009 Ó The Society of Population Ecology and Springer 2009. Abstract The population dynamics of the yellowjacket wasp (Vespula germanica Fabricus) in central Chile were analyzed for the first time. Using a simple Ricker logistic model and adding the effects of local weather variables (temperature and precipitation) and large-scale climate phenomena as El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM), we modeled the interannual fluctuations in nest density. The best model according to the Bayesian information criterion (BIC) included 1-year-lag negative feedback combined with the positive additive effects of ENSO and SAM. According to this model, yellowjacket nest density was favored by warm and dry winters, which probably influenced the survival of overwintering queens. Large-scale climatic variables [Southern Oscillation Index (SOI) and SAM] described the effect of exogenous factors in wasp fluctuations better than local weather variables did. Our results emphasize the usefulness of climate indices and simple theoretical-based models in insect ecological research. Keywords El Niño Southern Oscillation  Exogenous forcing  Large-scale climate indices  Southern Annular Mode  Vespula. Introduction The yellowjacket wasp (Vespula germanica Fabricius) is one of the most harmful invasive insect species in the. S. A. Estay (&)  M. Lima Center for Advanced Studies in Ecology and Biodiversity, Pontificia Universidad Católica de Chile, Casilla 114-D, Santiago 6513677, Chile e-mail: sestay@bio.puc.cl. world (Lowe et al. 2000). In all countries where the yellowjacket wasp has been introduced, it has become a serious problem for agriculture, apiculture, wildlife, and recreation activities (Harris and Oliver 1993; Matthews et al. 2000; Sackmann et al. 2000; Beggs 2001; Curkovic et al. 2004; Kasper 2004). In Chile, this insect was introduced more than 30 years ago (Edwards 1976; Archer 1998) and, during the last decade, high wasp densities have threatened recreational and tourist activities, and have also had a serious impact on agricultural work on fruit farms and in vineyards (Curkovic et al. 2004). To control and avoid the spread of this serious pest, it is essential to understand its population dynamics and how the combined effects of endogenous and exogenous factors determine its interannual fluctuations. Several investigations on this topic have been carried out in different places (Archer 1985, 2001; Barlow et al. 2002), some of them using local climate covariables as exogenous factors. The use of local climate variables in long-term studies began early in the 20th century (Elton 1924). For example, rainfall appears to show contrasting effects on wasp populations: in the northwest USA and New Zealand it seems to be negative (Akree and Reed 1981; Barlow et al. 2002), while in Tasmania rainfall appears to have a positive effect on fluctuations of common wasp populations (Madden 1981). On the other hand, the role of temperature in ectotherm performance has been shown to be unimodal, with an increasing phase at low and medium temperatures, and a decrease at high levels (Huey and Berrigan 2001). The temperature effects on the abundance of yellowjacket wasps have been described by Madden (1981), who found a positive relationship between temperature and wasp numbers in Tasmania. However, local variables are not the only way to evaluate climatic effects on population dynamics. Climate. 123.

(2) 290. indices are proxies for overall climate condition and represent ‘‘packages of weather’’ sensu Stenseth et al. (2003). In some cases, this allows a better approximation for studying the influence of climate on population dynamics than the use of local variables, and their use in ecological research has increased over the last 10 years (Stenseth et al. 2003). In southern South America, the two most relevant largescale climatic phenomena are El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). ENSO is a coupled ocean–atmosphere phenomenon with a strong influence on the interannual variability of temperature and precipitation in South America (Aceituno 1992; Andreoli and Kayano 2005; Barros et al. 2008; Garreaud et al. 2008). On the other hand, SAM is a pattern of tropospheric circulation variability centered on Antarctica, whose influence on temperature and precipitation regimens reaches to mid latitudes (Stenseth et al. 2003; Silvestri and Vera 2003; Gillett et al. 2006; Garreaud et al. 2008). Both phenomena exert important influences over ecological processes in South America (Holmgren et al. 2001; Jaksic 2001; Murúa et al. 2003a, b). To the best of our knowledge, no analysis using longterm time series has been carried out in southern South America to evaluate the relative contributions of endogenous and exogenous factors to the population dynamics of the yellowjacket wasp. In addition, studies relating wasp fluctuations to exogenous factors have used local weather variables, but the role of large-scale climatic variables, such as the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI), and the Southern Annular Mode (SAM), have not been explored. Here, we analyzed the population fluctuations of the yellowjacket wasp in central Chile using simple theoretical-based models in order to decipher the relative importance of local and large-scale climatic variables.. Methods Data Records of yellowjacket nests were taken from 1994 to 2005 in Rio Clarillo National Reserve, Metropolitan Region of Chile (33°410 S, 70°240 W, 860 m a.s.l., 13,085 ha extension; Fig. 1) by the staff of the National Forestry Corporation (CONAF). Data corresponded to the number of nests detected during several days in February (summer), when the greatest number of wasps is recorded, by means of systematic sampling throughout the reserve. The first flight of queens occurs in the spring (September), and the queen immediately begin to construct the nest in the soil or other cavities (Curkovic et al. 2004). The nest may contain more than 8,000 wasps, and at the end of the. 123. Popul Ecol (2010) 52:289–294. Fig. 1 Study site. Black point indicates the Rio Clarillo National Reserve (33°410 S, 70°240 W). summer when the nest exhibits its maximum activity it can contain 250–300 queens. These queens leave the nest at the end of the autumn and pass the winter protected in cavities or under the bark of trees until the next spring (Curkovic et al. 2004). The number of nests is a good proxy for the number of queens that have survived the winter months, and has been used previously for population dynamics studies of this pest (Archer 1985, 2001; Barlow et al. 2002). As a result of differences in sampling method (different number of days assigned each year for sampling), we used the standardized nest density, which was obtained by dividing the total number of nests counted during the sampling period by the number of days assigned for sampling each year. Moreover, to perform the time-series analysis, data were detrended by adjusting a linear model of the form Nt = a ? b 9 t, where Nt is the standardized nest density, t is time, and a and b are the estimated model parameters (Royama 1992; Berryman 1999; Kirchgässner and Wolters 2007). We used the residuals of this model plus the mean nest density as the detrended time series..

(3) Popul Ecol (2010) 52:289–294. Climatic data Local climatic data were collected from the climatic station located in the reserve. Records from this station for 1993 to 2005 were obtained from the Civil Aeronautical Office in Chile (DGAC). Winter and spring (June–July and September– October) average temperature (WAT and SAT, respectively) and precipitation of the previous year were used in this study (WPP and SPP, available from authors). This is because yellowjacket wasps form colonies with an annual monogynous lifecycle (Harris 1996); therefore, queens must survive the winter to create a new colony in the early spring. The Southern Oscillation Index (SOI), which is related to ENSO, and the SAM index were used in this study. Standardized annual SOI was obtained from http://www. cpc.ncep.noaa.gov/data/indices/soi. This index is the standardized difference in sea-level pressure between Tahiti and Darwin (Australia). Annual SAM index was obtained from http://www.nerc-bas.ac.uk/icd/gjma/sam.html. This index is calculated as described by Marshall (2003), using real sea-level pressure from six stations. Population dynamics diagnosis and modeling To determining the endogenous structure of the wasp population, we carried out an autoregressive analysis through the autocorrelation function (ACF) and the partial rate correlation function (PRCF), to determine the order of the feedback structure of these time series (Berryman and Turchin 2001). The population is dominated by first-order feedback (Fig. 3); therefore, as a starting point for modeling the reproductive function or R-function (Berryman 1999), we used a nonlinear version of the simple Ricker’s (1954) equation to model the basic influence of endogenous and exogenous forces on wasp population dynamics. This is an intraspecific competition model that has been used successfully in a previous study of this insect (Barlow et al. 2002). The nonlinear Ricker’s equation is:   ! Nt1 Q R ¼ Rm 1  ; ð1Þ K where Nt-1 is the standardized nest density at time t-1; R is the realized per-capita growth rate Rt = ln(Nt/Nt-1); Rm is the maximum per-capita growth rate estimated for the species; K is the carrying capacity or equilibrium density; and Q is a nonlinearity factor. In this model, we incorporated climatic perturbations using Royama’s framework (1992). According to Royama (1992) exogenous perturbations may be vertical, when the R-function is completely displaced through the y-axis, which means that the maximum reproductive capacity and the equilibrium density are modified by the action of the exogenous factor, or lateral,. 291. when the R-function is displaced through the x-axis, changing the equilibrium density but not the maximum reproductive capacity. Using this framework, we can build mechanistic hypotheses about the effects of climate on the wasp population. When a vertical effect is operating, an additive term must be included in the model, but in a lateral perturbation we replace the parameter K by a function of the exogenous variable (Estay et al. 2009). One or two climatic variables were included in each model, but only the best models using two climatic variables are shown. Models were fitted by nonlinear least squares using the nls library in the R program (R Development Core Team 2008, available at http://www.r-project.org) and ranked according to the Bayesian information criterion (BIC) or Schwarz (1978) criterion. For clarity, BIC weights were also included in the results. Minimum BIC was selected to determine the best model. Finally, simulations were carried out to elucidate the capacity of the models to describe the real dynamics. In spite of a lack of independent data to evaluate the simulations, this analysis allows one to visualize to some degree the predictive performance of the model. Simulations were carried out using only the first real value of the time series, observed variance, and random perturbations e(0, 0.1), and then running the algorithm using each model with their estimated parameters, to obtain the simulated 12-year values. Values of the root-mean-square prediction error (rmse; Sheiner and Beal 1981) were calculated to evaluate the predictive performance of the models. Smallest rmse values represent the best predictive performance.. Results Yellowjacket wasps in central Chile show clear first-order dynamics (Fig. 2a–c). First-order dynamics are characterized by sawtoothed oscillation (Fig. 2a), which is caused by negative feedback of density at time t-1 (Fig. 2c; Berryman 1999). PRCF showed that lag-1 is the main feedback process in the time series. Given that the basic structure for the analysis was a firstorder Ricker model, each climatic variable was additively incorporated (vertical effect sensu Royama 1992). The Ricker model plus the influence of ENSO and SAM was the best (Table 1). This model had the best BIC and the best predictive performance given by rmse. The simulations with this model closely reproduce the observed dynamics, especially the 2001 outbreak (Fig. 3). Other categories of Royama’s classification, such as lateral effects, were tested, but because of their poorer performance than that of the additive models, they are not shown. When ENSO and SAM were used alone in the models, the predictive performance decreased by roughly a half (from rmse 2.6 to 4.2 using only SAM, and 4.7 using only ENSO).. 123.

(4) 292. Popul Ecol (2010) 52:289–294. Fig. 2 a Time series of the standardized number of nests from 1994 to 2005 in Rio Clarillo National Reserve, central Chile. b Realized percapita growth rate (R = ln(Nt/ Nt-1) of the time series against the density of the previous year (Nt-1). c PRCF for standardized data. The major influence of first-order feedback structure is clear. Dashed lines represent a 12-year time series Bartlett band, which is a rough approximation to the 95% confidence interval. Table 1 Results of the modeling R2. Model. BIC. wBIC. rmse. R = 1.56 9 (1 - (Nt-1/6.39)0.50). 0.42. 31.54. 0.04. 5.9. R = 16.63 9 (1 - (Nt-1/3.57)0.04) ? 0.25 9 SAM. 0.67. 27.79. 0.24. 4.2. R = 0.94 9 (1 - (Nt-1/8.57)1.04) ? 0.56 9 ENSO. 0.58. 30.34. 0.07. 4.7. 0.52. R = 1.36 9 (1 - (Nt-1/5.35). ) ? 0.02 9 WAT. R = 10.68 9 (1 - (Nt-1/62.39)0.12) - 0.21 9 SAT. 0.42. 33.94. 0.01. 5.9. 0.45. 33.33. 0.01. 5.6. R = 2.17 9 (1 - (Nt-1/16.27)0.40) - 0.002 9 WPP. 0.54. 31.55. 0.04. 5.4. R = 1.31 9 (1 - (Nt-1/5.28)0.57) ? 0.002 9 SPP. 0.45. 33.35. 0.01. 5.5. R = 0.84 9 (1 - (Nt21/5.25)0.69) 1 0.45 9 ENSO 1 0.22 9 SAM. 0.77. 26.16. 0.54. 2.6 4.5. R = 1.17 9 (1 - (Nt-1/11.53). 0.91. 0.61. 32.16. 0.03. R = 4.56 9 (1 - (Nt-1/23.72)0.30) ? 0.001 9 SPP - 0.13 9 SAT. ) - 0.001 9 WPP ? 0.43 9 ENSO. 0.46. 35.55. 0.00. 5.4. R = 0.93 9 (1 - (Nt-1/3.11)0.36) - 0.002 9 WPP ? 0.101 9 WAT. 0.54. 33.82. 0.01. 5.7. The best model is shown in bold face. Estimated parameters for each model are shown in the model (see Eq. 1 for reference). BIC, BIC weights (wBIC), and root-mean-square prediction error (rmse) are shown. WAP and SAT are winter and spring average temperature, respectively; WPP and SPP are winter and spring total precipitation; ENSO is the annual SOI index and SAM is the annual SAM index (see the ‘‘Methods’’ section for details). The model ranking according to BIC is the same if we use Akaike information criterion for small sample size (AICc). Discussion Yellowjacket wasp population dynamics in central Chile are characterized by first-order oscillations, similar to those in their native habitat (Archer 1985, 2001) and in other countries where this insect has been introduced (Barlow. 123. et al. 2002). The mechanism behind this negative feedback has been suggested to be aggression and subsequent high mortality among queens during the first colony stage (Akree and Reed 1981; Archer 1985). Climate has been suggested as a key aspect for understanding the fluctuations of this insect (Akree and Reed.

(5) Popul Ecol (2010) 52:289–294. Fig. 3 Twelve-year simulation using the best model (Table 1). Points are the real values and the solid line represents the simulation and bars the 95% percentile confidence intervals from 10,000 simulations (see ‘‘Methods’’ for details). 1981; Madden 1981; Archer 1985; Barlow et al. 2002), but the specific climate variable and the sign of the effect vary with each study. Akree and Reed (1981) found that warm and dry springs have a positive influence on wasp abundance, but their analysis was based on anecdotal data, and a rigorous evaluation was absent. A negative effect of spring rainfall was found by Barlow et al. (2002), but Madden (1981) described a positive effect of autumn and spring rainfall on yellowjacket abundance, which was related to food availability in the early spring. The dissimilarity between these results suggests that the interaction between endogenous and exogenous factors depends on the whole environmental context in which the population is embedded. Our study is believed to be the first to describe the role of large-scale climatic variables on yellowjacket wasp fluctuations. The use of SAM and ENSO allowed us to test the influence of the overall climate condition (Stenseth et al. 2003) rather than the influence of a single variable. Previous studies have demonstrated that overwintering is a key step to explain wasp abundance in the following spring (Akree and Reed 1981; Archer 1985; Barlow et al. 2002). Several variables, or the interaction between them, may influence queen survival during the winter. Evidence of the influence of rainfall (Akree and Reed 1981; Barlow et al. 2002) and/or temperatures (Madden 1981) on the performance of yellowjacket wasps has been previously detected. Our simple model was able to explain almost all of the 80% variation in the population dynamics of yellowjacket wasps. 293. in central Chile. We think that the remaining unexplained variability may be related to resource availability. Positive values of the SOI index (ENSO) are associated with below-normal precipitation in northern and central Chile (Garreaud et al. 2008). On the other hand, positive values of the SAM index are associated with warm and dry events over southern South America (Gillett et al. 2006), especially with negatives anomalies in central Chile (Stenseth et al. 2003; Garreaud et al. 2008). Both results are in agreement with the previous studies of Akree and Reed (1981) and Barlow et al. (2002). The influence of ENSO in central Chile (Lima et al. 1999a, b, 2002) and SAM in southern Chile (Murúa et al. 2003a, b) has been detected previously in the dynamics of rodents. However, to the best of our knowledge, the present study is the first to detect an effect of these large-scale climatic phenomena on insect population dynamics in southern South America. Climate indices have been used successfully in other studies on the population dynamics of insects (e.g., Saldaña et al. 2007). As Stenseth et al. (2003) pointed out, the use of these indices helps us to reduce the complexity of the problem to one simple measure. For insect ecologists, these ‘‘packages of weather’’ represent an opportunity to analyze the sometimes complex interaction between climatic variables and insect physiology or behavior, and its consequences on population processes. This simplification increase our predictive ability (Stenseth et al. 2003); however, the exact mechanism involved in those processes remains unknown. In this vein, we think that our simple logistic model combined with the two large-scale climatic variables may be a useful tool for predicting and managing this pest in central Chile, but the general approach of this study can be applied in every problem related to the management of insect pests in the world. The use of simple theoretical-based models together with a clear representation of the effect of climatic variables allows one to increase the efficiency of control measures and diminishes their cost by concentrating operations at times when they are strictly necessary. We hope that these results will allow improvement of pest management against this insect in central Chile, especially by using the predictive ability of this model. Acknowledgments We thank the operators, forest rangers, and staff of the National Forestry Corporation, Metropolitan Region (CONAF RM) who collected the data for 12 years, especially to Mr. Luis Ulloa and Mr. José Barrera. S.E. acknowledges the financial support of the CONICYT Doctoral scholarship. S.E. and M.L. acknowledge financial support from FONDAP-FONDECYT grant 1501-0001 (Program 2).. References Aceituno P (1992) El Niño, the southern oscillation, and ENSO: confusing names for a complex ocean–atmosphere interaction. Bull Am Meteorol Soc 73:483–485. 123.

(6) 294 Akree R, Reed H (1981) Population cycles of yellowjackets (Hymenoptera: Vespinae) in the Pacific Northwest [Idaho; Oregon; Washington]. Environ Entomol 10:267–274 Andreoli R, Kayano M (2005) ENSO-related rainfall anomalies in South America and associated circulation features during warm and cold Pacific decadal oscillation regimes. Int J Climatol 25:2017–2030 Archer M (1985) Population dynamics of the social wasps Vespula vulgaris and Vespula germanica in England. J Anim Ecol 54:473–485 Archer M (1998) The world distribution of the Euro-Asian species of Paravespula (Hym, Vespine). Entomol Mon Mag 134:279–284 Archer M (2001) Changes in abundance of Vespula germanica and V. vulgaris in England. Ecol Entomol 26:1–7 Barlow N, Beggs J, Barron M (2002) Dynamics of common wasps in New Zealand beech forests: a model with density dependence and weather. J Anim Ecol 71:663–671 Barros V, Doyle M, Camilloni I (2008) Precipitation trends in southeastern South America: relationship with ENSO phases and with low-level circulation. Theor Appl Climatol 93:19–33 Beggs J (2001) The ecological consequences of social wasps (Vespula spp.) invading an ecosystem that has an abundant carbohydrate resource. Biol Conserv 99:17–28 Berryman A (1999) Principles of Population Dynamics and Their Applications. Stanley Thornes Publishers Ltd, Cheltenham Berryman A, Turchin P (2001) Identifying the density-dependent structure underlying ecological time series. Oikos 92:265–270 Curkovic T, Araya J, Guerrero M (2004) Avances en el manejo de la avispa chaqueta amarilla en Chile. Aconex 84:19–24 (in Spanish) Edwards R (1976) The world distribution pattern of the German wasp, Paravespula germanica (Hymenoptera: Vespidae). Entomol Ger 3:69–271 Elton C (1924) Periodic fluctuations in the numbers of animals: their causes and effects. J Exp Biol 2:119–163 Estay S, Lima M, Labra F (2009) Predicting insect pest status under climate change scenarios: combining experimental data and population dynamics modelling. J Appl Entomol 137:491–499 Garreaud R, Vuille M, Compagnucci R, Marengo J (2008) Presentday South American Climate. Palaeogeogr Palaeoclimatol Palaeoecol. doi:10.1016/j.palaeo.2007.10.032 Gillett N, Kell T, Jones P (2006) Regional climate impacts of the Southern Annular Mode. Geophys Res Lett 33:L23704 Harris R (1996) Frequency of overwintered Vespula germanica (Hymenoptera: Vespidae) colonies in scrubland-pasture habitat and their impact on prey. N Z J Zool 23:11–17 Harris R, Oliver E (1993) Prey diets and population densities of the wasps Vespula vulgaris and V. germanica in scrubland-pasture. N Z J Ecol 17:5–12 Holmgren M, Scheffer M, Ezcurra E, Gutiérrez J, Mohren G (2001) El Niño effects on the dynamics of terrestrial ecosystems. Trends Ecol Evol 16:89–94 Huey R, Berrigan D (2001) Temperature, demography, and ectotherm fitness. Am Nat 158:204–210 Jaksic F (2001) Ecological effects of El Niño in terrestrial ecosystems of western South America. Ecography 24:241–250 Kasper M (2004) The Population Ecology of an Invasive Social Insect, Vespula Germanica (Hymenoptera: Vespidae) in South Australia, Thesis, University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, Adelaide. 123. Popul Ecol (2010) 52:289–294 Kirchgässner G, Wolters J (2007) Introduction to modern time series analysis. Springer, Berlin Lima M, Keymer J, Jaksic F (1999a) ENSO-driven rainfall variability and delayed density-dependence cause rodent outbreaks in western South America: from demography to population dynamics. Am Nat 153:476–491 Lima M, Marquet P, Jaksic F (1999b) El Niño events, precipitation patterns, and rodent outbreaks are statistically associated in semiarid Chile. Ecography 22:213–218 Lima M, Stenseth N, Jaksic F (2002) Food web structure and climate effects in the dynamics of small mammals and owls in semiarid Chile. Ecol Lett 5:273–284 Lowe S, Browne M, Boudjelas S, De Poorter M (2000) 100 of the world’s worst invasive alien species: a selection from the global invasive species database. Published by The Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN), New Zealand Madden J (1981) Factors influencing the abundance of the European wasp (Paravespula germanica F.). J Aust Entomol Soc 20:59–65 Marshall G (2003) Trends in the Southern Annular Mode from observations and reanalyses. J Climate 16:4134–4143 Matthews R, Goodisman M, Austin A, Bashford R (2000) The introduced English wasp Vespula vulgaris (L.) (Hymenoptera: Vespidae) newly recorded invading native forests in Tasmania. Aust J Entomol 39:177–179 Murúa R, Gonzalez L, Lima M (2003a) Population dynamics of rice rats (a Hantavirus reservoir) in southern Chile: feedback structure and non-linear effects of climatic oscillations. Oikos 102:137–145 Murúa R, González L, Lima M (2003b) Second-order feedback and climatic effects determine the dynamics of a small rodent population in a temperate forest of South America. Popul Ecol 45:19–24 R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org Ricker W (1954) Stock and recruitment. J Fish Res Board Can 11:559–623 Royama T (1992) Analytical population dynamics. Chapman & Hall, London Sackmann P, D’Adamo P, Rabinovich M, Corley J (2000) Arthropod prey foraged by the German wasp (Vespula germanica) in NW Patagonia, Argentina. N Z Entomol 23:55–59 Saldaña S, Lima M, Estay S (2007) Northern Atlantic Oscillation NAO effects on the temporal and spatial dynamics of green spruce aphid populations at UK. J Anim Ecol 76:782–789 Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464 Sheiner L, Beal S (1981) Some suggestions for measuring predictive performance. J Pharmacokinet Phar 9:503–512 Silvestri G, Vera C (2003) Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys Res Lett 30:2115 Stenseth N, Ottersen G, Hurrell J, Mysterud A, Lima M, Chan K, Yoccoz N, Adlandsvik B (2003) Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Nino Southern Oscillation and beyond. Proc R Soc B 270:2087–2096.

(7)

Figure

Actualización...

Referencias

Actualización...