BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, researchlibraries, and research funders in the common goal of maximizing access to critical research.

Survivorship, Growth, and Detection of a Knob-scaled Lizard in Queretaro,MexicoAuthor(s): Claudia Molina-Zuluaga , Paul F. Doherty Jr. , J. Jaime Zúñiga-Vega , and J. Gastón Zamora-AbregoSource: Journal of Herpetology, 47(1):156-161. 2013.Published By: The Society for the Study of Amphibians and ReptilesDOI: http://dx.doi.org/10.1670/11-251URL: http://www.bioone.org/doi/full/10.1670/11-251

BioOne (www.bioone.org) is a nonprofit, online aggregation of core research in the biological, ecological, andenvironmental sciences. BioOne provides a sustainable online platform for over 170 journals and books publishedby nonprofit societies, associations, museums, institutions, and presses.

Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance ofBioOne’s Terms of Use, available at www.bioone.org/page/terms_of_use.

Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiriesor rights and permissions requests should be directed to the individual publisher as copyright holder.

Journal of Herpetology, Vol. 47, No. 1, 156–161, 2013Copyright 2013 Society for the Study of Amphibians and Reptiles

Survivorship, Growth, and Detection of a Knob-scaled Lizard in Queretaro, Mexico

CLAUDIA MOLINA-ZULUAGA,1,4 PAUL F. DOHERTY JR.,2 J. JAIME Z UNIGA-VEGA,1 AND J. GASTON ZAMORA-ABREGO3

1Departamento de Ecologıa y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico. Ciudad Universitaria 04510,Distrito Federal, Mexico

2Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523-1474 USA3Departamento de Ciencias Forestales, Universidad Nacional de Colombia – Sede Medellın, Calle 59A No. 63-020, Medellın, Colombia

ABSTRACT.—A deep understanding of the processes affecting the population dynamics of living organisms requires fine-scale analyses of basic

demographic parameters such as stage-specific survival and growth rates. In this study we estimated survival, detection, and transition rates

(growth) for different stages of the life cycle of a Knob-scaled Lizard of the genus Xenosaurus. We also examined potential sources of variationfor these parameters by means of a multiple-model inference framework. Our capture–mark–recapture data revealed that survival rates were

homogeneous among stage classes but markedly different between the rainy and dry seasons. Contrary to our expectation, survival probability

was higher during dry months. Detection probability varied considerably among stage classes and through time. Consistent with theoretical

predictions, the rate at which lizards moved from a particular stage class to the following (transition rates) varied among stage classes, with thefastest rates observed in yearlings and the slowest in adults. Also consistent with our predictions, we found a tendency toward faster transition

rates during rainy months. We discuss the potential causes and implications of the patterns of variation observed in these key demographic

parameters.

Given their direct impact on fitness, life-history traits aresensitive to environmental changes, and as such, they areusually under strong natural selection (Roff, 1992; Stearns,1992). Estimating these vital rates, which can have a significantrole in ecological and evolutionary processes, and determiningthe factors to which the vital rates are more sensitive, leads to abetter understanding of how individuals respond to environ-mental fluctuations. A variety of mark–recapture methods havebeen developed to estimate vital rates such as survival (u) andtransition probabilities between size or stage classes (w), whilecontrolling for the detection probability (Lebreton et al., 1992;Williams et al., 2002; Amstrup et al., 2005). Furthermore, basedon a multiple-model framework, testing alternative biologicalhypotheses about the factors influencing vital rates by evalu-ating the relative support of different competing models in themark–recapture data is possible (Lebreton et al., 1992; Burnhamand Anderson, 2002; Johnson and Omland, 2004).

We used mark–recapture data and a multiple-model inferenceframework to examine the vital rates of one population of arecently discovered Knob-scaled Lizard species (Xenosaurus sp.nov.) that is currently in the process being described formally(A. Nieto-Montes de Oca, unpubl. data). A previous study onthis population explored demographic trends using populationprojection matrices (Zamora-Abrego et al., 2010). This studyfound that the population had a tendency to increase (finitepopulation growth rate above unity) in years with favorableclimatic conditions, but it tended to decrease when conditionswere unfavorable (Zamora-Abrego et al., 2010). Becausepopulation trends result from changes in size- or stage-specificvital rates, a finer-scale analysis would provide insight into theparticular processes that affect the persistence probability of thepopulation. In the present study we focus on the samepopulation of Knob-scaled Lizards to estimate stage-specificvital rates and to explore some of the factors that may haveinfluence over them. For this purpose, we structured thepopulation into four stage classes: yearlings, juveniles, smalladults, and large adults. Specifically, we focus on the followingobjectives: 1) to estimate stage-specific probabilities of survival,

detection, and transition (i.e., growth) between stage classes and

2) to examine some of the factors that may promote variation in

these rates.

Several studies have shown that vital rates can exhibit

substantial plasticity in response to environmental factors such

as seasonality, temperature, precipitation, and food availability

(Roff, 1992; Stearns, 1992; Morrison and Hero, 2003; Boyce et al.,

2006). In addition, vital rates may vary depending on individual

characteristics such as age, sex, phenotype, or genotype (Laurie,

1990; Laurie and Brown, 1990; Lebreton et al., 1992). For these

reasons, we hypothesize about potential effects of some of these

factors on survival and transition probabilities. We predicted

that season (rainy or dry) would have a large effect on the

survival and transition probabilities because climatic differences

between seasons produce changes in food availability (Zuniga-

Vega et al., 2005). Specifically, we expected survival and

transition probabilities to be higher during the rainy season

because temperature is more stable, rainfall is abundant, and

more food is presumably available (Zuniga-Vega et al., 2005).

Additionally, we expected survival and transition probabilities

to differ among yearlings, juveniles, small adults, and large

adults. The smaller-stage classes (yearlings and juveniles)

should experience lower survival compared to larger-stage

classes (adult). In contrast, given the relatively fast growth rates

associated with the early phases of the life cycle (Andrews,

1982), the higher transition probabilities should occur in the

smallest stage classes (yearlings and juveniles).

MATERIALS AND METHODS

Study area and study species.—Xenosaurus sp. nov. is a medium-

sized lizard (average female size is [6 SE] 106.9 6 0.5 mm snout–

vent length [SVL]; average male size is 99.7 6 0.7 mm SVL) with

a flattened morphology associated with its strict crevice-dwelling

habits. Females are larger than males, although males generally

have wider and longer heads (Lemos-Espinal et al., 2004;

Zamora-Abrego et al., 2007, 2010). As in other xenosaurid

species, individuals from this species are viviparous, thermal-

conforming, and sit-and-wait foragers (Ballinger et al., 1995;

Lemos-Espinal et al., 1997; 2004).

4Corresponding Author. E-mail: clamozu@gmail.comDOI: 10.1670/11-251

To estimate demographic parameters, we selected a 2-ha plotnear the town of Tilaco, in northeast Queretaro, Mexico, at anelevation of 1,184 m (218100N, 998100W). We chose this particularplot because it was the only site within the range of distributionof this new taxon with enough individual lizards to yield robustsurvival and transition estimates. The site is located in atransitional zone between oak forest and subperennial tropicalforest (Zamudio et al., 1992). This location has a mean annualrainfall of 773 mm (La Lagunita weather station, ComisionNacional del Agua, 10 km NE of the study site), with a wetseason from June to October (80% of total annual precipitation)and a dry season from November to May (20% of total annualprecipitation). Mean monthly temperatures are [6 SE] 21.98 6

0.518C during the wet season and 19.67 6 0.688C during the dryseason.

Field methods.—We used a capture–mark–recapture fieldexperiment to estimate demographic parameters. We visited thestudy site approximately once every 50 days from October 2001to October 2004 for a total of 22 capture occasions throughout thisstudy period. On each visit we surveyed all possible microhab-itats (i.e., rock crevices) that individual lizards could inhabit.Lizards were removed from crevices by hand and individuallymarked by toe-clipping upon first capture. For each capturedlizard, we recorded sex and SVL. After collecting these data,lizards were released immediately to the same crevice where theywere captured.

Estimating demographic parameters.—We used our mark–recap-ture data to estimate stage-specific survival probabilities bymeans of a multistate framework (Brownie et al., 1993; Lebretonet al., 2009) implemented in program MARK (White andBurnham, 1999). This software uses maximum likelihoodprocedures to estimate demographic parameters (e.g., probabil-ities of survival (u), detection (p), and transition between states(w)). In particular, our estimated survival probabilities corre-spond to ‘‘apparent survival,’’ given that our multistate modelcannot distinguish death from permanent emigration (Brownie etal., 1993). Since we did not catch every lizard at every timeperiod, we had to account for detection probabilities in ourmodeling approach. Besides, we did not have exact lengthmeasurements when we did not catch a lizard. Thus, westructured the population into four stage classes (yearlings,juveniles, and two adult classes), which we considered states inour multistate model and that we predicted would experiencedifferent vital rates. We estimated apparent survival anddetection for individuals within each of these categories andtransition between these categories. This approach accounts notonly for detection probabilities less than 1, but also foruncertainty in transitions between stage classes when individualsare not detected. Yearlings were lizards from size at birth to sizeat 1 yr old (51.6–67 mm SVL). Juveniles were defined as lizardslarger than yearlings, but not yet at breeding size (68–79 mm SVLfor males and 68–91 mm SVL for females). The first adult stageclass (small adults) corresponded to individuals from size atmaturity to the size at which further body growth was negligible(80–103 mm SVL for males and 92–103 mm for females). Thesecond adult stage class (large adults) corresponded to the largestlizards (larger than 103 mm SVL, Zamora-Abrego et al., 2007;2010). These four categories, which were defined based on size,represent distinct ontogenetic stages for xenosaurid lizards(Zuniga-Vega et al., 2007; Rojas-Gonzalez et al., 2008; Zamora-Abrego et al., 2010).

We marked and followed 145 lizards during the study period.Of these, 14 were marked as yearlings, 29 as juveniles, 61 as

small adults, and 41 as large adults. We recaptured 139individuals at least once during the study.

Model set.—We defined a candidate model set where survival(u) and detection probabilities (p) could either be constant (.) orvary as a function of capture occasion (t), stage class (g; yearlings,juveniles, small adults, large adults), or season (s; rainy or dry).We also considered additive effects of these factors (e.g., g + s)affecting u and p, except that we never included t and s in thesame model as these two factors are alternative ways of modelingtemporal variation. Similarly, transition probability betweenstage classes (w) could either be constant (.) or vary as a functionof season (s). We fitted our mark–recapture data to all modelsthat were derived from all possible combinations of factorsaffecting u, p, and w. These models represent the a priorihypotheses that we had about potential sources of variation forthese parameters.

Model selection was based on the Akaike’s informationcriterion (AIC), which is a measure of model likelihood andparsimony (Akaike, 1973). Specifically, we used an adjustedversion of the AIC for small sample sizes (AICc, Burnham andAnderson, 2002). The lowest AICc score indicates the modelwith the most parsimonious fit to the mark–recapture data.However, models with a difference in their AICc scores (DAICc)lower than 2 were considered to have similar support in thedata. In addition, we calculated model-specific Akaike weights,which measure the relative support or weight of evidence foreach model in the data (all weights sum to 1 for the entire modelset; Amstrup et al., 2005). Because we had a balanced model setwe also calculated cumulative AICc weights for each factor(stage class, capture occasion, and season). This was done bysumming the weights of all models that contained the variableof interest. We calculated weighted averages for survival,detection, and transition rates according to the AICc weight ofeach competing model (Burnham and Anderson, 2004). Thesemodel-weighted estimates of u, p, and w incorporate theuncertainty in the process of model selection and, thus, aremore robust than those derived from any single model alone(Johnson and Omland, 2004). All estimates are given on amonthly basis to facilitate biological interpretation.

RESULTS

Our most supported model was one in which survivalprobability varied as a function of season (u[s]), detectionprobability varied as a function of both capture occasion andstage class (p[t + g]), and transition probabilities between stageclasses were constant except for the transition between smalladults and large adults, which varied as a function of season[wYJ(.) wJA(.) wAB(s), where Y = yearling, J = juvenile, A =young adult, and B = large adult; AICc weight = 0.24; Table 1].However, there was uncertainty in the process of modelselection and two other models had similar fit compared tothe top model (DAICc < 2; Table 1). The second-best-supportedmodel (DAICc = 0.75, AICc weight = 0.16) differed from the firstone only in the transition probability from juveniles to smalladults being a function of season (Table 1). The third-best-supported model (DAICc = 1.94, AICc weight = 0.09) differedfrom the top model in survival being constant across all stagesclasses and through time (Table 1). Together, these three modelshad a cumulative AICc weight equal to 0.49.

Apparent survival.—The effect of season (AICc cumulativeweight = 0.719; Table 2) on the survival probability was evidentfor individuals of all stage classes (Fig. 1). However, apparent

SURVIVAL PROBABILITY OF A XENOSAURID LIZARD 157

survival was lower during the rainy season (Fig. 1), which wasopposite to what we expected. During the rainy season survivalwas (mean 6 SE) 0.952 6 0.018 for all stage classes. In contrast,during the dry season survival was higher: 0.983 6 0.013 for allstage classes. We found little evidence for effects of stage class(cumulative AICc weight = 0.066) or temporal variation(cumulative AICc weight = 0.003) on the survival probabilities(Table 2).

Detection probability.—Detection probability varied widelyamong stage classes (cumulative AICc weight = 0.983) andthrough time (cumulative AICc weight = 0.988; Table 2).Detection per sampling occasion varied between 0.117 6 0.053and 0.628 6 0.066 and was lowest for yearlings (averagedetection rate for yearlings: 0.240 6 0.090) and highest for largeadults (average detection rate for large adults: 0.366 6 0.076; Fig.2). Intermediate average values for detection probability occurredin juveniles (0.285 6 0.076) and small adults (0.247 6 0.061).

Transition between stage classes.—Season had the strongest effecton the transition probability between small and large adults anda smaller effect on the transition probability between juvenilesand small adults (Table 2, Fig. 3). A higher transition ratebetween these stage classes occurred during the rainy season(Fig. 3). The average transition rate between small and largeadults was the lowest (0.039 6 0.016), whereas the averagetransition rate between yearlings and juveniles was the highest(0.158 6 0.064). The average transition probability from juvenilesto small adults had an intermediate value (0.129 6 0.049; Fig. 3).

DISCUSSION

Apparent survival.—An important effect of season (rainy/dry)on survival was demonstrated in this study. However, contrary toour prediction, survival probability of all stage classes was lowerduring the rainy season, even though during these months (June

to October) temperatures are less extreme and food availability ishigher (Zuniga-Vega et al., 2005). We propose a tentativeexplanation for this low survival during the rainy season basedon the temporal variation observed in the activity patterns oflizard species (Rose, 1981; Adolph and Porter, 1993, 1996). Duringrainy or warm seasons lizards are more active because days arelonger and temperatures are appropriate for reproductive andforaging activities (Rojas-Gonzalez et al., 2008). This increasedactivity also increases the amount of time that lizards are underpredation risk (assuming that mortality caused by predation ishigher during active periods compared to those in which lizardsremain inactive and hidden; Rose, 1981; Adolph and Porter, 1993,1996). Thus, longer activity periods may reduce survival ratesduring such rainy or warm seasons.

In addition, reproductive activities of xenosaurid lizards takeplace during the rainy season (June to November; Ballinger etal., 2000; Zamora-Abrego et al., 2007). Specifically for Xenosau-rus sp. nov., matings occur between October and February andbirths take place between June and August, after a gestationperiod that lasts between 6 and 7 months (Zamora-Abrego,2003; Zamora-Abrego et al., 2007). Both matings and birthsentail high energetic costs to lizards, which in turn results in atrade-off between current reproduction and survival (Shine,1980; Stearns, 1992; Miles et al., 2000; Cox and Calsbeek, 2010).A recent study on the congeneric species Xenosaurus grandisgrandis demonstrated that during rainy months (i.e., when lategestation and parturitions occur) females experience a consid-erable decrease in survival rate, presumably associated with lategestation and parturition (Zuniga-Vega, 2011). Therefore, thesereproductive processes may also have adverse effects on ourstudied species and, hence, may explain the lower survival ratesobserved during the rainy season. In particular, sex-specificreproductive costs would result in sex-specific survival rates.Adult females might be the ones suffering lower survival duringrainy months because late gestation and parturitions in

TABLE 1. Top 10 models describing the probabilities of survival (u), recapture (p), and transition among stage classes (w) for a population of theKnob-scaled Lizard Xenosaurus sp. nov. AICc corresponds to the Akaike’s information criterion adjusted for small sample sizes. DAICc corresponds tothe difference in the AICc score of each model with respect to the top model. AICc weights are measures of relative support for each model in the data.The number of parameters calculated by each model is also shown. We modeled parameters as constant (.), or varying by capture occasion (t), season(s), and/or stage class (g). wYJ, wJA, and wAB correspond to the transition from yearlings to juveniles, from juveniles to small adults, and from smalladults to large adults, respectively.

Model AICc DAICc AICc weights Parameters

u(s) p(t + g) wYJ(.) wJA(.) wAB(s) 4,759.26 0.00 0.24 30u(s) p(t + g) wYJ(.) wJA(s) wAB(s) 4,760.01 0.75 0.16 31u(.) p(t + g) wYJ(.) wJA(.) wAB(s) 4,761.20 1.94 0.09 29u(s) p(t + g) wYJ(s) wJA(.) wAB(s) 4,761.52 2.26 0.08 31u(.) p(t + g) wYJ(.) wJA(s) wAB(s) 4,761.91 2.65 0.06 30u(s) p(t + g) wYJ(.) wJA(.) wAB(.) 4,762.25 2.99 0.05 29u(s) p(t + g) wYJ(s) wJA(s) wAB(s) 4,762.28 3.02 0.05 32u(s) p(t + g) wYJ(.) wJA(s) wAB(.) 4,763.06 3.80 0.04 30u(.) p(t + g) wYJ(s) wJA(.) wAB(s) 4,763.45 4.19 0.03 30u(.) p(t + g) wYJ(.) wJA(.) wAB(.) 4,763.94 4.68 0.02 28

TABLE 2. Cumulative Akaike’s information criterion adjusted for small sample sizes (AICc) weights for the factors that were included in thecandidate model set as sources of variation for the survival (u), recapture (p), and transition (w) probabilities for a population of Xenosaurus sp. nov.Factors g, t, and s correspond to the effects of stage class, capture occasion, and season, respectively. wYJ, wJA, and wAB correspond to the transitionfrom yearlings to juveniles, from juveniles to small adults, and from small adults to large adults, respectively.

u pwYJ wJA wAB

Factor g t s g t s s s s

Cumulative AICc weights 0.066 0.003 0.719 0.983 0.988 0.010 0.244 0.407 0.814

158 C. MOLINA-ZULUAGA ET AL.

Xenosaurus sp. nov. occur during a large part of the rainy season

(between May and August; Zamora-Abrego, 2003; Zamora-

Abrego et al., 2007).

Alternatively, the relatively low apparent survival observed

during rainy months might be explained by increased dispersal.

Recall that apparent survival does not distinguish death from

permanent emigration (Lebreton et al., 1992). To test this

possibility we need to examine temporal and spatial movement

patterns of this xenosaurid species.

We also expected survival rates to be lowest in yearlings and

juveniles. Our mark–recapture data did not support this

prediction either. Survival rates were fairly similar among stage

classes. This result is remarkable given that in several other

species of lizards, the early phases of the life cycle experience

considerably lower survival probabilities compared to repro-

ductive individuals (e.g., Ballinger, 1973; Zuniga-Vega et al.,

2008; Rodrıguez-Romero et al., 2011). The strict crevice-dwelling

habit of these lizards may account for the high and constant

survival observed throughout the life cycle. Apparently, crevices

are safe refuges and, in general, xenosaurid lizards rarely go out

of their crevices (Lemos-Espinal et al., 2004; Zuniga-Vega et al.,

2007; Rojas-Gonzalez et al., 2008).

Detection probability.—Our results demonstrated that these

lizards had relatively low detection probabilities (Fig. 2). This

contrasts with previous demographic studies focused on this and

other species of the genus Xenosaurus (Zuniga-Vega et al., 2007;

Rojas-Gonzalez et al., 2008; Zamora-Abrego et al., 2010), in which

detection probabilities were assumed to be close to one. The

reason for this assumption was the belief that these lizards

occupy only one or two rock crevices during their entire lifetime

(Lemos-Espinal et al., 2003; Zamora-Abrego et al., 2010). Either

they move more than we thought among several suitable

crevices, or they indeed exhibit high fidelity to one or two

crevices and low vagility; our results indicate that these lizards

can easily pass unnoticed to field researchers. Low detection

rates, if not accounted for, can cause bias in demographic

parameters including survival (Lebreton et al., 1992; Tuyttens et

al., 1999).

Detection probabilities varied among capture occasions and

among stage classes (Fig. 2). Temporal variation in detection

probability may reflect slight differences in sampling effort per

capture occasion, as well as differences in the number of field

observers. In turn, differences in detection probability among

stage classes indicate that lizards of different sizes have unequal

chances of being detected or that lizards of different ontogenetic

stages exhibit distinct habits and behaviors. For instance,

yearlings have the smallest sizes and may go unnoticed during

the inspection of crevices, yielding lower detection probabilities.

In contrast, large adults may be more conspicuous because of

FIG. 2. Model-averaged detection probabilities per stage class and capture occasion for the lizard Xenosaurus sp. nov. (A) yearlings, (B) juveniles,(C) small adults, and (D) large adults. Error bars represent 95% confidence intervals.

FIG. 1. Model-averaged monthly survival probabilities per seasonand stage class for the lizard Xenosaurus sp. nov. Error bars represent95% confidence intervals.

SURVIVAL PROBABILITY OF A XENOSAURID LIZARD 159

their largest sizes or territorial behaviors (Ballinger et al., 1995;

Smith et al., 1997), yielding higher detection probabilities.

Transition among stage classes.—Our predictions about transition

(growth) rates among stage classes were supported by our mark–

recapture data. First, we found a marked effect of season on the

transition probability between small and large adults and

between juveniles and small adults (Fig. 3). For these stage

classes, higher transition probabilities occurred during the rainy

season in comparison with the dry season. The rainy season has

higher temperatures, which lead to increased metabolic rates and

enhanced digestive efficiency for ectothermic organisms (Dun-

ham et al., 1989; Sinervo and Adolph, 1994; Angilletta, 2001;

Chen et al., 2003). In addition, food for these lizards is more

abundant during these wet months (Zuniga-Vega et al., 2005). All

this allows for faster rates of body growth (Smith and Ballinger,

1994; Lemos-Espinal et al., 2003; Zuniga-Vega et al., 2005) that in

turn result in a higher proportion of individuals transiting from

one stage class to another, in comparison with dry months.

Second, transition probability between stage classes de-

creased considerably as the size of lizards increased, such that

yearlings had higher transition probability than juveniles and

these in turn had higher transition probability than small adults.

This result was also consistent with our prediction. Models of

body growth demonstrated that the congeneric species X.grandis grandis experiences maximum growth rates at small

sizes (Zuniga-Vega et al., 2005). As lizards become larger, they

reduce the amount of resources allocated to growth in order to

invest in other physiological processes such as reproduction,

maintenance, and storage (Shine, 1980; Stearns 1989, 1992;

Zuniga-Vega et al., 2005). Apparently, this same pattern of

ontogenetic variation in the amount of resources allocated to

body growth also occurs in Xenosaurus sp. nov., given that the

proportion of individuals growing to larger size classes

decreased as a function of ontogenetic stage.

A noticeable result was that the effect of season on transition

rates was only evident for juveniles and small adults, whereas

yearlings grew at the same rate in both seasons. Lizards of

medium and large sizes might be more susceptible to limited

resources such as those expected during dry months. This

hypothesis deserves further experimental research in which the

metabolic costs are compared among stage classes.

Acknowledgments.—We thank the Consejo Nacional de Cienciay Tecnologıa (CONACyT) and the Direccion General deEstudios de Posgrado, Universidad Nacional Autonoma deMexico, for academic and financial support to CM-Z and JGZ-Aduring their graduate studies. We are deeply grateful to J.Lemos-Espinal and I. Rojas-Gonzalez, who have been collabo-rating with us in the ecological study of this new species.Fieldwork was conducted under permit number 04555 (SEM-ARNAT-Mexico). Financial support was provided by researchprojects IN200102-PAPIIT, IN216199-PAPIIT, and 40797-Q-CONACyT.

LITERATURE CITED

ADOLPH, S. C., AND W. P. PORTER. 1993. Temperature, activity and lizardlife histories. The American Naturalist 142:273–295.

———. 1996. Growth, seasonality and life histories: age and size atmaturity. Oikos 77:267–278.

AKAIKE, H. 1973. Information theory and an extension of the maximumlikelihood principle. In B. N. Petran and F. Csaaki. (eds.),International Symposium on Information Theory, 2nd ed., pp. 267–281 Acadeemiai Kiadi, Budapest, Hungary.

AMSTRUP, S. C., T. L. MCDONALD, AND B. F. J. MANLY. 2005. Handbook ofCapture-Recapture Analysis. Princeton University Press, Princeton,NJ.

ANDREWS, R. M. 1982. Patterns of growth in reptiles. In C. Gans and F. H.Pough (eds.), Biology of the Reptilia, Vol. 13. Physiology D, pp. 272–320. Academic Press, New York.

ANGILLETTA, M. J., JR. 2001. Thermal and physiological constraints onenergy assimilation in a widespread lizard (Sceloporus undulatus).Ecology 82:3044–3056.

BALLINGER, R. E. 1973. Comparative demography of two viviparousiguanid lizards (Sceloporus jarrovi and Sceloporus poinsetti). Ecology54:269–283.

BALLINGER, R. E., J. A. LEMOS-ESPINAL, AND G. R. SMITH. 2000. Reproductionin females of three species of crevice-dwelling lizards (genus:Xenosuaurus) from Mexico. Studies on Neotropical Fauna andEnvironment 35:179–183.

BALLINGER, R. E., J. A. LEMOS-ESPINAL, S. SANOJASARABIA, AND N. R. COADY.1995. Ecological observations of the lizard, Xenosaurus grandis inCuautlapan, Veracruz, Mexico. Biotropica 27:128–132.

BOYCE, M. S., C. V. HARIDAS, AND C. T. LEE. 2006. Demography in anincreasingly variable world. Trends in Ecology and Evolution 21:141–148.

BROWNIE, C., J. E. HINES, J. D. NICHOLS, K. H. POLLOCK, AND J. B. HESTBECK.1993. Capture-recapture studies for multiple strata including non-Markovian transitions. Biometrics 49:1173–1187.

BURNHAM, K. P., AND D. R. ANDERSON. 2002. Model selection andmultimodel inference. A practical information-theoretic approach.2nd ed. Springer-Verlag, New York.

———. 2004. Multimodel inference. Understanding AIC and BIC inmodel selection. Sociological Methods Research 33:261–304.

CHEN, X. J., X. F. XU, AND X. JI. 2003. Influence of body temperature onfood assimilation and locomotor performance in White-striped GrassLizards, Takydromus wolteri (Lacertidae). Journal of Thermal Biology28:385–391.

COX, R. M., AND R. CALSBEEK. 2010. Severe costs of reproduction persist inAnolis lizards despite the evolution of a single-egg clutch. Evolution64:1321–1330.

DUNHAM, A. E., B. W. GRANT, AND K. L. OVERALL. 1989. Interfaces betweenbiophysical and physiological ecology and the population ecology ofterrestrial vertebrate ectotherms. Physiological Zoology 62:335–355.

JOHNSON, J. B., AND K. S. OMLAND. 2004. Model selection in ecology andevolution. Trends in Ecology and Evolution 19:101–108.

LAURIE, W. A. 1990. Effects of the 1982-83 El Nino-Southern OscillationEvent on marine iguana (Amblyrhynchus cristatus Bell, 1825)populations on Galapagos. Elsevier Oceanography Series 52:361–380.

LAURIE, W. A., AND D. BROWN. 1990. Population biology of marineiguanas (Amblyrhynchus cristatus). II. Changes in annual survivalrates and the effects of size, sex, age, and fecundity in a populationcrash. Journal of Animal Ecology 59:529–544.

FIG. 3. Model-averaged monthly transition probabilities betweenstage classes per season for the lizard Xenosaurus sp. nov. Y to Jrepresents the transition from yearlings to juveniles, J to A represents thetransition from juveniles to small adults, and A to B represents thetransition from small adults to large adults. Error bars represent 95%confidence intervals.

160 C. MOLINA-ZULUAGA ET AL.

LEBRETON, J. D., K. P. BURNHAM, J. CLOBERT, AND D. R. ANDERSON. 1992.Modeling survival and testing biological hypotheses using markedanimals: a unified approach with case studies. Ecological Mono-graphs 62:67–118.

LEBRETON, J. D., J. D. NICHOLS, R. J. BARKER, R. PRADEL, AND J. A. SPENDELOW.2009. Modeling individual animal histories with multistate capture-recapture models. Advances in Ecological Research 41:87–173.

LEMOS-ESPINAL, J. A., G. R. SMITH, AND R. E. BALLINGER. 1997. Naturalhistory of Xenosaurus platyceps, a crevice dwelling lizard fromTamaulipas, Mexico. Herpetological Natural History 5:181–186.

———. 2003. Variation in growth and demography of a KnobscaledLizard (Xenosaurus newmanorum: Xenosauridae) from a seasonaltropical environment in Mexico. Biotropica 35:240–249.

———. 2004. Aspects of the ecology of a distinct population ofXenosaurus platyceps from Queretaro, Mexico. Amphibia-Reptilia25:204–210.

MILES, D. B., B. SINERVO, AND W. A. FRANKINO. 2000. Reproductive burden,locomotor performance, and the cost of reproduction in free ranginglizards. Evolution 54:1386–1395.

MORRISON, C., AND J.-M. HERO. 2003. Geographic variation in life-historycharacteristics of amphibians: a review. Journal of Animal Ecology72:270–279.

RODRIGUEZ-ROMERO, F., G. R. SMITH, F. MENDEZ-SANCHEZ, O. HERNANDEZ-GALLEGOS, P. SANCHEZ-NAVA, AND F. R. MENDEZ-DE LA CRUZ. 2011.Demography of a semelparous, high-elevation population ofSceloporus bicanthalis (Lacertilia: Phrynosomatidae) from the Nevadode Toluca volcano, Mexico. Southwestern Naturalist 56:71–77.

ROFF, D. A. 1992. The Evolution of Life Histories: Theory and Analysis.Chapman and Hall, New York.

ROJAS-GONZALEZ, R. I., C. P. JONES, J. J. ZUNIGA-VEGA, AND J. A. LEMOS-ESPINAL. 2008. Demography of Xenosaurus platyceps (Squamata:Xenosauridae): a comparison between tropical and temperatepopulations. Amphibia-Reptilia 29:245–256.

ROSE, B. 1981. Factors affecting activity in Sceloporus virgatus. Ecology 62:706–716.

SHINE, R. 1980. ‘‘Costs’’ of reproduction in reptiles. Oecologia 46:92–100.SINERVO, B., AND S. C. ADOLPH. 1994. Growth plasticity and thermal

opportunity in Sceloporus lizards. Ecology 75:776–790.SMITH, G. R., AND R. E. BALLINGER. 1994. Temporal and spatial variation in

individual growth in the spiny lizard, Sceloporus jarrovi. Copeia 1994:1007–1013.

SMITH, G. R., J. A. LEMOS-ESPINAL, AND R. E. BALLINGER. 1997. Sexualdimorphism in two species of Knob-scaled Lizards (genus Xenosau-rus) from Mexico. Herpetologica 53:200–205.

STEARNS, S. C. 1989. Trade-offs in life history evolution. FunctionalEcology 3:259–268.

———. 1992. The Evolution of Life Histories. Oxford University Press,New York.

TUYTTENS, F. A. M., D. W. MCDONALD, R. DELAHAY, L. M. ROGERS, P. J.MALLINSON, C. A. DONELLY, AND C. NEWMAN. 1999. Differences intrappability of European Badgers Meles meles in three populations inEngland. Journal of Applied Ecology 36:1051–1062.

WHITE, G., AND K. P. BURNHAM. 1999. Program MARK: survivalestimation from populations of marked animals. Bird Study 46:S120–S138.

WILLIAMS, B. K., J. D. NICHOLS, AND M. J. CONROY. 2002. Analysis andmanagement of vertebrate populations. Academic Press, San Diego.

ZAMORA-ABREGO, J. G. 2003. Historia Natural, Biologıa Reproductiva,Habitos Alimentarios y Area de Actividad de una Poblacion deXenosaurus platyceps, al Noreste del Estado de Queretaro, Mexico.Unpubl. M. Sc. thesis, Facultad de Estudios Superiores, UniversidadNacional Autonoma de Mexico., Iztacala, Mexico.

ZAMORA-ABREGO, J. G., J. J. ZUNIGA-VEGA, AND A. NIETO-MONTES DE OCA.2007. Variation in reproductive traits within the lizard genusXenosaurus. Journal of Herpetology 41:630–637.

ZAMORA-ABREGO, J. G., Y. M. CHANG, J. J. ZUNIGA-VEGA, A. NIETO-MONTES

DE OCA, AND J. B. JOHNSON. 2010. Demography of a Knob-scaledLizard in northeastern Queretaro, Mexico. Herpetologica 66:39–51.

ZAMUDIO, R. S., J. RZEDOWSKI, E. CARRANZA, AND G. C. DE RZEDOWSKI. 1992.La Vegetacion del Estado de Queretaro (Panorama Preliminar).Instituto de Ecologıa, Centro Regional del Bajıo, Patzcuaro,Michoacan, Mexico.

ZUNIGA-VEGA, J. J. 2011. Estimating potential reproductive costs in thesurvival of a xenosaurid lizard. Herpetological Journal 21:117–129.

ZUNIGA-VEGA, J. J., R. I. ROJAS-GONZALEZ, J. A. LEMOS-ESPINAL, AND M. E.PEREZ-TREJO. 2005. Growth ecology of the lizard Xenosaurus grandis inVeracruz, Mexico. Journal of Herpetology 39:433–443.

ZUNIGA-VEGA, J. J., T. VALVERDE, R. I. ROJAS-GONZALEZ, AND J. A. LEMOS-ESPINAL. 2007. Analysis of the population dynamics of an endangeredlizard (Xenosaurus grandis) through the use of projection matrices.Copeia 2007:324–335.

ZUNIGA-VEGA, J. J., F. R. MENDEZ-DE LA CRUZ, AND O. CUELLAR. 2008.Demography of the lizard Sceloporus grammicus: exploring temporalvariation in population dynamics. Canadian Journal of Zoology 86:1397–1409.

Accepted: 26 March 2012.

SURVIVAL PROBABILITY OF A XENOSAURID LIZARD 161