Skip to main content

Climatic niche differences among Zootoca vivipara clades with different parity modes: implications for the evolution and maintenance of viviparity

Abstract

Parity mode (oviparity/viviparity) importantly affects the ecology, morphology, physiology, biogeography and evolution of organisms. The main hypotheses explaining the evolution and maintenance of viviparity are based on bioclimatic predictions and also state that the benefits of viviparity arise during the reproductive period. We identify the main climatic variables discriminating between viviparous and oviparous Eurasian common lizard (Zootoca vivipara) occurrence records during the reproductive period and over the entire year.

Analyses based on the climates during the reproductive period show that viviparous clades inhabit sites with less variable temperature and precipitation. On the contrary, analyses based on the annual climates show that viviparous clades inhabit sites with more variable temperatures.

Results from models using climates during reproduction are in line with the “selfish-mother hypothesis”, which can explain the success of viviparity, the maintenance of the two reproductive modes, and why viviparous individuals cannot colonize sites inhabited by oviparous ones (and vice versa). They suggest that during the reproductive period viviparity has an adaptive advantage over oviparity in less risky habitats thanks to the selfish behaviour of the mothers. Moreover, the results from both analyses stress that hypotheses about the evolution and maintenance of viviparity need to be tested during the reproductive period.

Background

Reproductive mode (e.g. oviparity, viviparity) is an important biological trait that may affect the ecology, morphology, physiology, biogeography and evolution of organisms [1,2,3]. While the embryonic development of many oviparous organisms mainly depends on environmental conditions, those of viviparous organisms can at least partially be controlled by mothers. For example, viviparous mothers can control intra-uterine temperature by means of thermoregulation and habitat choice [4], and they provide embrios with nutrients during their development [5]. Two main hypotheses have initially been put forward to explain the evolution and maintenance of viviparity in reptiles. The “cold climate hypothesis” (CCH [6]) advocates that viviparity is an adaptation to cold environments because it allows mothers to raise embryonic temperature above the environmental temperature, what is especially advantageous if environmental temperatures are not high enough for the development and survival of embryos. The “maternal manipulation hypothesis” (MMH) advocates that the main advantage of viviparity resides on its positive effect on offspring viability and development in less favourable environmental conditions [7, 8], which include low and high ambient temperatures, and other unfavourable aspects relevant to embryonic development [9], such as highly variable and unpredictable environments [10, 11].

Both hypotheses exclusively explain the maintenance and evolution of viviparity by a positive effect on offspring fitness. However, selection favours strategies that increase an individuals’ rather than its offspring’s fitness [12, 13]. Thus, adaptations which are positive for mothers should be favoured, regardless of how these adaptations affect the offspring’s fitness [14, 15]. More specifically, selection can increase a mother’s fitness at the expense of her offspring’s fitness, because a mother’s fitness depends on her lifespan, and on how she resolves the trade-offs between survival and reproduction and between the mother’s and the offspring’s optimal maternal investment [13, 14, 16, 17]. Based on these ideas, a novel hypothesis, the “selfish-mother hypothesis” (SMH), may explain the evolution and maintenance of viviparity.

The SSH predicts that females favour their own lifetime reproductive success and survival, rather than her offspring’s sucess [18]. Because gravidity of viviparous females lasts much longer than that of oviparous females [19, 20], viviparous females in risky habitats (e.g., habitats that prolongate gravidity) are more likely exposed to a trade-off between favouring their survival and fitness over that of her offspring, resulting in offspring of lower viability and in extreme cases abortion of the eggs [21]. The hypothesis further predicts that in less risky habitats viviparous females are not exposed to this trade-off and that they produce more offspring of higher viability than oviparous females, which may favour the evolution and maintenance of viviparity.

Despite the differences among the three hypotheses, they all predict that viviparity is an adaptation to the environmental conditions prevailing during the reproductive period, rather than those prevailing during the entire year (e.g. [22, 23]). Studies addressing the maintenance and evolution of viviparity from a biogeographic or macroecological perspective and including taxa belonging to one [23, 24] or many families [22, 25], mostly provided evidence in favour of the CCH hypothesis. However, several observations are not congruent with the CCH: first, viviparity is relatively common in tropical snakes and thus it is not restricted to species inhabiting the temperate zone [26]; and second, the early evolution of viviparity in squamates happened during the Jurassic, i.e., during a warmer period [2].

Moreover, several studies highlighted that current distributions may not reflect the climate at the time and place where viviparity evolved [6, 22]. This suggests that current data may unravel parameters explaining the maintenance of viviparity, but not necessarily those explaining its evolution. In other words, the more time passed since the evolution of viviparity, the lower will be the likelihood that current climatic and distributional data allow to infer the drivers of its evolution. More specifically, the drivers of events that happened tens of millions of years ago may be less likely to pin down (e.g. [23, 24]) and Pyron and Burbrink [27] suggested that the use of detailed species-level data and taxa with more recently evolved viviparity may increase the likelihood of detecting the drivers of their evolution. Moreover, species-level data may avoid biases inherent to studies including many different species [27]. Thus, species with bimodal reproduction might be good study candidates [25].

Here we tested for differences in the inhabited climatic niche among oviparous and viviparous clades of the Eurasian common lizard Zootoca vivipara (Lichtenstein, 1823). Zootoca vivipara is a small lizard species with bimodal reproductive mode that exhibits the largest geographical and the northernmost distribution of terrestrial reptiles [28]. It consists of six genetic clades (Fig. 1), that evolved in the last ≤4.4 million years [29] and thus more recently than the transitions to viviparity considered in previous studies (in Lambert and Wiens [23], Pincheira-Donoso et al. [24], Watson et al. [25], and Feldman et al. [22], most of the analysed transitions from oviparity to viviparity happened more than 8 Mya). To understand which hypothesis best explains the current distribution, we used fine-scale distributional data of Z. vivipara, niche comparison tests, and discrimination models of parity mode (oviparous versus viviparous) based on climatic data. According to the CCH [6], we predicted that on average viviparous lizards inhabit locations with colder average or minimum temperatures during reproduction (Table 1; Fig. 1- prediction CCP1). According to the MMH [9], we predicted that on average viviparous lizards inhabit locations with less favourable, i.e. colder (Fig. 1- prediction MMP1) or warmer locations (Fig. 1- prediction MMP2 [9]), and/or more variable climates (e.g., more variable in temperature and precipitation) than oviparous populations (Table 1; Fig. 1- prediction MMP3). According to the SMH, we predicted that viviparous females inhabit locations in which they are less likely exposed to a trade-off between their own survival and fitness, and that of their offspring. Because viviparous Z. vivipara females take almost twice as long to lay their eggs (about one month more, for an additional 6–8 embryonic developmental stages [19, 20]) and because gestion of viviparous Z. vivipara females is not facultative [19], we predicted that viviparous Z. vivipara inhabit locations with lower climatic variability (Table 1; Fig. 1- prediction SMP1), in which embryonic development is faster and egg laying is earlier [10, 11]. The shorter gravidity period reduces the time during which the developing eggs negatively affect a female’s locomotion, which reduces a female’s predation risk (e.g, [30, 31]). Moreover, females are also less likely to invest a lot of energy into reproduction without success. According to the SMH, we also predicted that on average viviparous females may live in warmer climates, where embryonic development is faster (Table 1; Fig. 1- prediction SMP2 [32, 33]). Finally, if the annual climate reflects the climate prevailing during reproduction, we predicted that differences in climatic niches among viviparous and oviparous populations during the reproductive period will be similar to those based on annual values [22].

Fig. 1
figure1

Schematic distribution of the Zootoca vivipara clades in Eurasia and climatic predictions derived from the three hypothesis explaining the evolution of viviparity. Dots on the map refer to populations used to disentangle among the hypotheses and dot colour to the genetic clade affiliation; green: clade A, blue: clade B, purple: clade C, orange: clade D, pink: clade E, and yellow: clade F. Distributions of viviparous clades are coloured in light green and those of oviparous lizards are in dark green. Letters on the geographic map refer to genetic clades [29]. On the right of the map, climatic predictions (abbreviated as ‘P’), derived from three hypothesis explaining the evolution and maintenance of viviparity. Predicted range differences of optimal incubation temperature and differences in average incubation temperature (black ‘-‘within range) for viviparous (light green) and oviparous (dark green) lizards are shown for each hypotheses. \( \overline{X} \) corresponds to average incubation temperature, σ2 to the variance in incubation temperature, ‘>’ indicates that values are bigger in oviparous clades, ‘=’ indicates that there exists no differences among parity modes, and ‘<’ indicates bigger values in viviparous clades. The predictions for each of the three hypotheses are abbreviated using lowercase numbers. For simplicity, only the predictions related to temperature are shown, while the general predictions are stated in the article’s text

Table 1 Combinations of differences between parity modes in variables describing variability (second column) and average temperature (third column) and their support for a given hypothesis explaining the evolution of viviparity (Fig. 1). Abbreviations: ↑vivi: viviparous > oviparous, ↓vivi: viviparous < oviparous, ‘-‘: no differences, CCH: cold-climate hypothesis, MMH: maternal manipulation hypothesis, SMH: selfish mother hypothesis, letters and numbers in brackets correspond to the predictions listed in Fig. 1 (e.g. MMP1: Manternal Manipulation Prediction 1)

Results

During the reproductive period, principal component analyses of the climatic variables rendered two axes, which accounted for 76.4% of the variation (Fig. 2). The first axis (PC1, 63.7% of the variation explained) was positively related with the seasonality of temperature and precipitation (BIO4 and BIO15, Fig. 3a, for the meaning of the abbreviations of the bioclimatic variables see Supporting Information Appendix S1, S2), and negatively with the other five variables (Fig. 3a). Precipitation seasonality (BIO15) exhibited the lowest loading, while all other variables showed medium loadings (Fig. 3a). The second axis (PC2) explained 12.7% of the variation (Fig. 2), and it mainly represents precipitation seasonality (BIO15) and, to a lesser extent, temperature seasonality (BIO4; Fig. 3b). On average, clade B occupies the coldest areas and areas with the highest seasonal and the lowest diurnal climatic variability, whereas clade D inhabits the warmest areas with the lowest seasonal and the highest diurnal climatic variability (Fig. 3c).

Fig. 2
figure2

Percentage of variation explained by the PCA axes for the reproductive period

Fig. 3
figure3

Results of the principal component analysis (PCA) carried out on the climatic variables measured during the reproductive period. Loadings of the climatic variables (for details about the climatic variables see Appendix S1) in the first (a: PC1) and second (b: PC2) PCA-axis. c Ordination space delimited by the two first principal component axes (PC1 and PC2) with centroids of each clade. Green: clade A, blue: clade B, purple: clade C, orange: clade D, pink: clade E, and yellow: clade F

Niche similarity tests showed that C-F exhibit significantly higher Dobs than expected by chance (Table 2), and a relatively high overlap (Dobs = 0.655 [34]). In half of the comparisons, clades exhibited significant non-equivalent niches (Table 2).

Table 2 Results of the niche similarity and equivalency tests for the reproductive period. Below the diagonal, pair-wise niche similarity (Dobs) scores are given and asterisks indicate a significant two-tailed niche similary test (* p < 0.05; in the significant pair, Dobs was higher than expected by chance). Above the diagonal, significances of pair-wise equivalency tests are shown (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant; in all significant pairs, Dobs was lower than expected by chance)

Cross-validation points to a tree of four leaves (three splits) and a correct classification rate of 82.7% (Fig. 4). 54 out of 78 oviparous records (69.2%) and 99 out of 107 viviparous records (92.5%) were correctly predicted. 21 out of 37 occurrences of clade A (56.7%), 33 out of 41 occurrences of clade B (80.5%), 25 out of 26 occurrences of clade D (96.1%), 43 out of 47 occurrences of clade E (91.5%), 12 out of 15 occurrences of clade F (80%), and 100% of the occurrences of clade C were correctly classified (Fig. 4). Globally, variability related variables were most influential in the classification tree (Fig. 5), namely temperature seasonality (BIO4) and isothermality (BIO3). BIO4 was important in all splits, BIO3 was important in all but one (split 2, Fig. 5), and precipitation seasonality (BIO15) played the major role in split 2. In each split, lower values of the main variable were associated with viviparity (Fig. 4). In split 1, viviparity was associated with lower temperature seasonality and higher relative diurnal variability (BIO3, isothermality). In split 2, viviparity was associated with lower seasonality in precipitation and temperature, and in split 3 with lower relative diurnal variability and higher temperature seasonality (Fig. 5). In each split, the most important variable (BIO4 in split 1, BIO15 in split 2, and BIO3 in split 3) was twice as important as the second most relevant variable (Fig. 5), indicating that viviparous populations occupy habitats with lower temperature and precipitation seasonality, and lower isothermality compared to oviparous populations (Fig. 5).

Fig. 4
figure4

Classification tree for the reproductive period resulting from recursive partitioning with parity mode (oviparous vs. viviparous) as response variable and the seven climatic covariates (Supporting Information Appendix S1) as predictors. In each split, the variable that decreased the impurity the most is indicated and its threshold value is given (BIO4, units: degrees Celsius; BIO15, units: millimetres; BIO3, units: percent). Grey leaves represent the cases classified as oviparous and white leaves represent those classified as viviparous. Numbers within leaves correspond to the number of correctly and incorrectly classified cases (left and right values, respectively). Numbers next to each leave, refer to the number of correctly (underlined) and wrongly classified (not underlined) cases per clade

Fig. 5
figure5

Importance (% of improvement in deviance; y-axis) of the climatic variables (x-axis; for details on their meaning see Appendix S1) in the global classification tree (panel a) and in the splits 1–3 of Fig. 4 (panel b-d). Variables whose effect is larger than expected by chance, i.e., those whose importance is larger than 100/(number of variables), are indicated by grey shaded bars. Arrows above significant bars indicate that the climatic variable was negatively correlated with the main variable, i.e., the variable explaining the highest % of the variance. That is, if the main variable’s effect was negative, the effect of the variable indicated with an arrow was positive, or vice versa

As in the analyses based on the reproductive period, principal component analyses of the annual climatic variables rendered two axes (Appendix S3, Figs. 6 and 7), which accounted for a high percentage of the variation (Supporting Information Appendix S3, Fig. 6). The annual analyses (Supporting Information Appendix S3) showed a general low niche overlap between clades, and only the overlap between clades E and F exhibited significantly higher Dobs than expected by chance (Table 3). Moreover, clades exhibited non-equivalent niches in 9 out of 15 comparisons (Table 3). The classification tree showed a high correct classification rate (77.8%), which was similar to that of the analyses based on the reproductive period. Clades C, D, and F were correctly classified, while the entire clade A was misclassified (Fig. 8). In contrast to the analyses based on the reproductive period, viviparous populations occupied locations with higher temperature seasonality (BIO4) than oviparous populations (Fig. 9).

Fig. 6
figure6

Percentage of variation explained by the PCA axes for the annual period

Fig. 7
figure7

Results of the principal component analysis (PCA) for the annual period. Loadings of the climatic variables (for details about the climatic variables see Appendix S1) in the first (a) and second (b) PCA-axis. c Ordination space delimited by the two first principal component axes (PC1 and PC2) with centroids of each clade. Green: clade A, blue: clade B, purple: clade C, orange: clade D, pink: clade E, and yellow: clade F

Table 3 Results of the niche similarity and equivalency test for the annual period. Below the diagonal, pair-wise niche similarity (Dobs) scores are given and asterisks indicate a significant two-tailed niche similary test (* p < 0.05; in the significant pair, Dobs was higher than expected by chance). Above the diagonal, significance of pair-wise equivalency tests are shown (* p ≤ 0.05, ** p ≤ 0.01, NS: not significant; in all significant pairs, Dobs was lower than expected by chance).
Fig. 8
figure8

Classification tree for the annual period resulting from recursive partitioning with parity mode (oviparous vs. viviparous) as response variable and the six annual climatic covariates (Appendix S1) as predictors. The variable that decreased the impurity the most is indicated and its threshold value is given (BIO4, units: degrees Celsius). Grey leaves represent the cases classified as oviparous and white leaves represent those classified as viviparous. Numbers within leaves correspond to the number of correctly and incorrectly classified cases (left and right values, respectively). Numbers next to each leave, refer to the number per clade of correctly (underlined) and wrongly classified cases

Fig. 9
figure9

Importance (% of improvement in deviance, y-axis) of the climatic variables (x-axis, for details on their meaning see Appendix S1) in the classification tree of Fig. 8, whose effect is bigger than 1%. Variables whose effect is larger than expected by chance, i.e., those whose importance is larger than 100/(number of variables), are indicated by grey shaded bars. Arrows above significant bars indicate that the climatic variable was negatively correlated with the main variable, i.e., the variable explaining the highest % of the variance. That is, if the main variable’s effect was negative, the effect of the variable indicated with an arrow was positive, or vice versa

Discussion

This study investigates differences in the climatic niches of all described viviparous and oviparous clades of Zootoca vivipara to shed light on the selective forces favouring the evolution and maintenance of viviparity. More specifically, we investigated whether the observed differences in the inhabited climatic niches agree with the predictions of the “cold-climate hypothesis” (CCH [6]), the “maternal manipulation hypothesis” (MMH [7]), and/or the “selfish-mother hypothesis” (SMH [12]). In comparison to previous studies on the evolution of viviparity, this study investigates the evolution of viviparity in a single species, which evolved viviparity relatively recently (≤ 4.4 Mya [29]). Both characteristics potentially increase the likelihood of detecting the drivers of viviparity [22].

Niche equivalence tests showed that in most clade pairs the climatic niches during reproduction were not equivalent and only clades C and F had significantly similar niches (Table 2). Consequently, niche differentiation among clades was strong (see low niche overlap values and many non-equivalent niches; Tables 2). The average climatic niche of clade B (the youngest and oviparous clade [29]) was characterized by the coldest and most variable climate during the reproductive period (Fig. 3c). The viviparous clade D inhabits the warmest average climate (Fig. 3c). Recursive partitioning (Fig. 4) distinguished oviparous from viviparous occurrences with a surprisingly high correct classification rate of 82.7%. Seasonality related variables (BIO3, BIO4, and BIO15) were the most important determinants of parity mode, while variables related with temperature range and climatic averages (BIO2, BIO6, minTmax and meanTmin) had low predictive power (Figs. 4 and 5). Viviparous clades exhibited lower values of BIO4, BIO15, and BIO3 (i.e., three variables related to climatic variability) than oviparous clades (Fig. 4), and thus they inhabit climates with lower variability. These results contrast with the predictions of the “cold-climate hypothesis” (CCH: viviparous populations inhabit colder climates; Table 1; Fig. 1- prediction CCP1 [6]). The results also contrast with the “maternal manipulation hypothesis” (MMH), which predicts that viviparous populations preferentially inhabit locations with less favourable climates, e.g. habitats with more variable temperature (Fig. 1- prediction MMP3) and/or with higher (Fig. 1- prediction MMP2) or lower average temperature (Table 1; Fig. 1- prediction MMP1 [9]). However, the results are in line with the prediction from the “selfish-mother hypothesis” (SMH: viviparous populations inhabit less risky habitats, e.g. on average less variable climates; Table 1; Fig. 1- prediction SMP1 [12]). Riskier habitats, e.g. climates with higher climatic variability, lead to prolonged gestation [10, 11, 35] and more oxidative damage in mothers [36], and small temperature differences can extend embryonic development by many weeks [11]. Prolonged gestation also increases the female’s exposure to predation [e.g, 30, 31] and viviparous females will therefore favour their own fitness over that of their offspring. Habitats with lower climatic variability are less risky for viviparous females, since the embryonic development is faster and the risk to invest a lot of energy into reproduction without success is lower compared to more variable habitats. Moreover, less variable temperatures during offspring development lead to offspring with higher stamina and increased exploratory behaviour [11], both being positive for the neonate [11]. This suggests that less variable habitats are beneficial for viviparous females and also for their offspring, which points to an adaptive advantage of viviparity.

The higher climatic variability prevailing during the reproductive period in populations of oviparous common lizards may explain why viviparous lizards cannot expand their distributions southwards. In other words, why they cannot colonize habitat inhabited by clade A or B [29]. In contrast, our analyses suggest that oviparous lizards might be able to live in less risky climates that exhibit lower climatic variability. Consequently, population mixing at the suture zone of viviparous and oviparous populations should be frequent. However, recent genetic evidence shows that introgression among viviparous and oviparous lizards is generally low [1, 37], potentially due to reinforcement [1] and higher maternal fitness of viviparous, compared to oviparous females [38]. Moreover, in a contact zone oviparous Z. vivipara had shorter telomere length than viviparous Z. vivipara [39] and telomer length is negatively linked with extinction risk [16], suggesting that viviparous Z. vivipara may have an additional advantage when both reproductive modes are in contact. The finding that viviparous Z. vivipara inhabit less variable habitats during reproduction is thus congruent with the idea that viviparity provided an adaptive advantage over oviparity in less risky habitats.

Clade A inhabits the Southern limit of Z. vivipara’s distribution and has been suggested to be the most vulnerable clade to climate change [40]. Northward movements of clade A are hindered by the Alps’ main ridge, which consists of insuperable high mountains and perpetual ice over large parts of clade A’s northern distribution limit. Moreover, in the Eastern Alps, northward movements are limited by the presence of other clades (clades: C, E, F [1]). The insuperable northern limit and the fact that clade A exhibits the highest misclassification rate (Figs. 4 and 8), suggest that geographic confinement exists in clade A. This confinement may have favoured a faster change of clade A’s ecological niche compared to the other clades, which may additionally complicate the detection of the drivers of the evolution of viviparity.

The correct classification rate of the classification tree including the reproductive period was higher than that including the annual period (+ 5%; Figs. 4 and 8). The former tree included 4 while the latter tree included only 2 leaves, and in both classification trees the seasonality of temperature (BIO4) was the most important variable in the first split (Figs. 4 and 8). However, during the reproductive period (May–July) lower seasonality of temperature was associated with viviparity, while lower seasonality of temperature during the entire year was associated with oviparity. This shows that studies based on annual climates do not necessarily provide the same results as those based on climates prevailing during the reproductive period, and the results based on one or the other data may not be qualitatively the same. This result contrasts to earlier findings [22], and it stresses that the three hypotheses about the evolution of viviparity need to be tested considering the reproductive period.

Conclusions

In summary, here we investigated which hypotheses best explain the evolution and maintenance of viviparity at the intraspecific level of Zootoca vivipara. The currently inhabited climatic niche of viviparous and oviparous Z. vivipara is best explained by the SMH [12], while the predictions of other hypotheses (Table 1, Fig. 1) are incongruent with the currently inhabited climatic niches. Our findings can explain why vivparity may have evolved and why both parity modes are maintained. They suggest that viviparity may have an adaptive advantage over oviparity in less risk climatic conditions (prevailing during the reproductive period) and that viviparous lizards may not be able to invade habitats that are currently occupied by oviparous congeners. This clearly contrasts to previous claims [22, 23, 27] and it suggests that taxa with more recently evolved viviparity may increase the likelihood of detecting the drivers of its evolution. These findings have important implications for the study and understanding of the evolution of reproductive modes in vertebrates, and together with the results of other studies [22, 23], they suggest that in many taxa the evolution of viviparity may have happened too long ago, to detect the ecological drivers that promoted its evolution and success.

Methods

Used samples

The European common lizard Zootoca vivipara (Lichtenstein, 1823) exhibits two reproductive modes and it consists of six genetic lineages (clades A-F) that inhabit large parts of Eurasia (Supporting Information Appendix S4; Fig. 1; and [29]). Two clades (clades A and B) are oviparous, and four clades are viviparous (C-F [29]). Clade A inhabits the Southern Alps (Northern Italy, Slovenia and Southern Austria) and is the oldest linage (divergence form the other clades: 4.4 Mya; 95% CI = 4.2–2.6 Mya [29]). Clade B inhabits Southern France and Northwest Iberia and diverged 2.0 (1.6–2.41 95% CI) Mya [29]. Clade C and F inhabit Austria, clade D, the Carpathians, North and East Eurasia, and clade E, Eastern and Western Europe and the Southern Balkan [29]. These clades diverged between 2.0 (1.6–2.4) and 2.2 (1.8–2.6) Mya [29]. Zootoca vivipara is the terrestrial reptile with the world’s widest and the farthest north distribution [28]. It inhabits temperate, boreal, alpine, Atlantic and continental climates [29] and altitudes from sea level up to over 2400 m above sea level. It passes the winter in hibernacula 2–13 cm below ground [41, 42], where minimum winter temperatures are not lower than a few degrees below zero degrees Celsius [41]. Female common lizards emerge from hibernation in early spring (from March onwards [43]). Mating happens right after emergence [43, 44] and in oviparous populations, egg laying happens on average 1 month after mating. In oviparous populations, females frequently produce two clutches per year, which are laid between May and July [45]. Time of gravidity (from ovulation to oviposition) is temperature dependent [11] and lasts between 25 and 40 days [11]. In viviparous females, the gestation period lasts on average 2 month and parturition of soft-shelled eggs containing fully developed offspring occurs between beginning of June and the end of July [19, 46, 47]. Offspring hatch within one day and are thereafter autonomous, corresponding to ovoviviparous reproduction [48]. In viviparous Z. vivipara, the duration of the gestation is not determined by an embryonic signal [19]. Gestation time depends on temperature [11, 49], and females may abort their eggs, and if they do not lay their eggs they may die (personal observations in viviparous and oviparous clades). Moreover, inadequate temperature and humidity regimes during gestation and incubation inevitably lead to deleterious effects on embryos [11, 36].

Characterization and comparison of climatic niches

For this study we used coordinates from 185 Z. vivipara populations from [29], hereafter referred to ‘occurrence records’, belonging to all major Z. vivipara lineages (clades A-F) and covering the majority of the known natural Eurasian distribution (Eurasia; Supporting Information Appendix S4; Fig. 1). The environmental PCA (PCA-env) method proposed by Broennimann et al. [34] was used to characterize and compare the realized climatic niches of the Z. vivipara clades, since this method performs better than other techniques [34]. The convex hull of the above mentioned 185 Z. vivipara populations was considered as the extent of the analyses, since within this area it is reasonable to assume that the species was in contact with the prevailing environmental conditions [50, 51]. This method reduces the amount of locations with non-informative absences [52], a factor shown to importantly affect overlap metrics [53]. Two types of analyses were conducted that tested for climatic differences among clades (1) during the entire year, and (2) during the reproductive period (May–July). Six non-redundant climatic variables related to average climate, diurnal and seasonal variability were considered for the analyses of the annual, and seven variables were considered for the analyses of the reproductive period (Supporting Information Appendix S1). For the correspondence of the two sets of variables see Supporting Information Appendix S1 and for the justification of their use see Supporting Information Appendix S2. All variables were obtained from the Worldclim database [54] at a resolution of 30-arc sec (0.0083 degrees) and monthly Worldclim data was used to derive the different climatic variables for the reproductive period (Supporting Information Appendix S1, S2).

First, 20′000 pixels (0.19% of all pixels) were randomly selected within the area delimited by the convex hull, and a principal component analysis (PCA) was conducted on the environmental variables within the randomly selected pixels and those with occurrence records (20′000 + 185 = 20′185 pixels). Second, a grid of 100 × 100 cells was laid over the ordination space delimited by the two first PCA axes, and a kernel density function was used to create occurrence density plots for each clade. Third, overlaps in occurrence density between two clades were calculated for each pair of clades using the Schoener’s D metric (Dobs), since this metric performs better than other overlap indexes [53]. Forth, tests of niche similarity and equivalency were performed according to Warren et al. [55] and Broennimann et al. [34]. Briefly, for the similarity tests, for each pair of clades the occurrence density surface of both clades was randomly shifted in the ordination space and D was computed (Dsim [56]). This procedure was repeated 1000 times to generate the distribution of Dsim, and to test whether Dobs significantly differed from random expectation using a two-tailed test. In the case of the equivalency test, the occurrences of a pair of clades were randomly re-assigned to each clade and the niche overlap (Dsim) was calculated. For each pair of clades this procedure was repeated 1000 times to obtain the distribution of Dsim and to test whether Dobs is significantly smaller than expected by chance. The analyses were performed with the ecospat package [56] for R [57].

Discrimination between parity modes

Recursive partitioning was performed to identify the main variables discriminating between occurrence records of oviparous and viviparous specimens. Classification trees [58] were run in R using the rpart package [59] with parity mode (oviparous vs. viviparous) as binomial response variable and the environmental variables as covariates. First, a tree was built using the Gini coefficient as impurity measure. The complexity parameter was set to zero, the maximum number of surrogate splits to [number of covariates minus one], and a 10-fold cross-validation was used to estimate the relative error. This tree was pruned using the 1-[Standard Error] criterion to get the final parsimonious tree [58]. The importance of each environmental variable across the entire tree and in each split was measured by the decrease in impurity when using the covariate as primary or surrogate variable (see [60] for further details). Again, these analyses were run for the annual as well as the reproductive period (see Supporting Information Appendix S1, S2).

Availability of data and materials

Used coordinates stem from Horreo et al. [29] and climatic data stems from the Worldclim database [54].

Abbreviations

σ 2 :

(variance)

\( \overline{X} \) :

(average)

BIO2:

Mean diurnal temperature range

BIO3:

Isothermality

BIO4:

Temperature seasonality

BIO5:

Maximum temperature of warmest month

BIO6:

Minimum temperature of coldest month

BIO10:

Mean temperature of warmest quarter

BIO15:

Precipitation seasonality

CCH:

Cold climate hypothesis

CCP1 :

Cold climate prediction 1

CI:

Confidence interval

Dobs :

Observed overlap in occurrence density between two clades

Dsim :

Simulated overlap in occurrence density between two clades

meanTmin:

mean of the monthly minimum temperature

minTmax:

Minimum of the monthly maximum temperature

OVI:

Oviparous

Mya:

Millon years ago

MMH:

Maternal manipulation hypothesis

MMP1 :

Maternal manipulation prediction 1

PCA-env:

Environmental principal components, calibrated on the entire environmental space

PC1:

Principal component one

PC2:

Principal component two

SMH:

Selfish-mother hypothesis

SMP1 :

Selfish-mother prediction 1

VIVI:

Viviparous

References

  1. 1.

    Horreo JL, Breedveld MC, Lindtke D, Heulin B, Surget-Groba Y, Fitze PS. Genetic introgression among differentiated clades is lower among clades exhibiting different parity modes. Heredity. 2019;123(2):264–72. https://doi.org/10.1038/s41437-019-0201-7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Pyron RA, Burbrink FT. Early origin of viviparity and multiple reversions to oviparity in squamate reptiles. Ecol Lett. 2014;17(1):13–21. https://doi.org/10.1111/ele.12168.

    Article  PubMed  Google Scholar 

  3. 3.

    Thompson MB, Blackburn DG. Evolution of viviparity in reptiles: introduction to the symposium. Herpetol Monogr. 2006;20(1):129–30. https://doi.org/10.1655/0733-1347(2007)20[129:EOVIRI]2.0.CO;2.

    Article  Google Scholar 

  4. 4.

    Shine R. Incubation regimes of cold-climate reptiles: the thermal consequences of nest-site choice, viviparity and maternal basking. Biol J Linn Soc. 2004;83(2):145–55. https://doi.org/10.1111/j.1095-8312.2004.00376.x.

    Article  Google Scholar 

  5. 5.

    Jones SM, Swain R. Placental transfer of 3H-oleic acid in three species of viviparous lizards: a route for supplementation of embryonic fat bodies? Herpetol Monogr. 2006;20(1):186–93. https://doi.org/10.1655/0733-1347(2007)20[186:PTOHOA]2.0.CO;2.

    Article  Google Scholar 

  6. 6.

    Shine R. Reptilian reproductive modes: the oviparity-viviparity continuum. Herpetologica. 1983;39:1–8.

    Google Scholar 

  7. 7.

    Shine R. A new hypothesis for the evolution of viviparity in reptiles. Am Nat. 1995;145(5):809–23. https://doi.org/10.1086/285769.

    Article  Google Scholar 

  8. 8.

    Shine R. Manipulative mothers and selective forces: the effects of reproduction on thermoregulation in reptiles. Herpetologica. 2012;68(3):289–98. https://doi.org/10.1655/HERPETOLOGICA-D-12-00004.1.

    Article  Google Scholar 

  9. 9.

    Shine R. Evolution of an evolutionary hypothesis: a history of changing ideas about the adaptive significance of viviparity in reptiles. J Herpetol. 2014;48(2):147–61. https://doi.org/10.1670/13-075.

    Article  Google Scholar 

  10. 10.

    Masó G, Kaufmann J, Clavero H, Fitze PS. Age-depedent effects of moderate differences in environmental predictability forecasted by climate change, experimental evidence from a short-lived lizard (Zootoca vivipara). Sci Rep. 2019;9(1):15546. https://doi.org/10.1038/s41598-019-51955-7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Foucart T, Heulin B, Lourdais A. Small changes, big benefits: testing the significance of maternal thermoregulation in a lizard with extended egg retention. Biol J Linn Soc. 2018;125(2):280–91. https://doi.org/10.1093/biolinnean/bly105.

    Article  Google Scholar 

  12. 12.

    Schwarzkopf L, Andrews RM. Are moms manipulative or just selfish? Evaluating the “maternal manipulation hypothesis” and implications for life-history studies of reptiles. Herpetologica. 2012;68(2):147–59. https://doi.org/10.1655/HERPETOLOGICA-D-11-00009.1.

    Article  Google Scholar 

  13. 13.

    Stearns SC. The evolution of life histories. Oxford: Oxford University Press; 1992.

    Google Scholar 

  14. 14.

    Marshall DJ, Uller T. When is a maternal effect adaptive? Oikos. 2007;116(12):1957–63. https://doi.org/10.1111/j.2007.0030-1299.16203.x.

    Article  Google Scholar 

  15. 15.

    Wilson AJ, Pilkington JG, Pemberton JM, Coltman DW, Overall ADJ, Byrne KA, et al. Selection on mothers and offspring: whose phenotype is it and does it matter? Evolution. 2005;59(2):451–63. https://doi.org/10.1111/j.0014-3820.2005.tb01003.x.

    Article  PubMed  Google Scholar 

  16. 16.

    Dupoué A, Rutschmann A, Le Galiard JF, Clobert J, Angelier F, Marciau C, et al. Shorter telomeres precede population extinction in wild lizards. Sci Rep. 2017;7(1):16976. https://doi.org/10.1038/s41598-017-17323-z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Le Galliard JF, Cote J, Fitze PS. Lifetime and intergenerational fitness consequences of harmful male interactions for female lizards. Ecology. 2008;89(1):56–64. https://doi.org/10.1890/06-2076.1.

    Article  PubMed  Google Scholar 

  18. 18.

    Schwarzkopf L, Andrews RM. "selfish mothers" use "maternal manipulation" to maximize lifetime reproductive success. Herpetologica. 2012;68(3):308–11. https://doi.org/10.1655/HERPETOLOGICA-D-12-00034.1.

    Article  Google Scholar 

  19. 19.

    Bleu J, Le Galliard JF, Meylan S, Massot M, Fitze PS. Mating does not influence reproductive investment, in a viviparous lizard. J Exp Zool A. 2011;415:458–64.

    Article  Google Scholar 

  20. 20.

    Bleu J, Heulin B, Haussy C, Meylan S, Massot M. Experimental evidence of early costs of reproduction in cospecific viviparous and oviparous lizards, vol. 25; 2012. p. 1264–74.

    Google Scholar 

  21. 21.

    Radder RS, Elphick MJ, Warner DA, Pike DA, Shine R. Reproductive modes in lizards: measuring fitness consequences of the duration of uterine retention of eggs, vol. 22; 2008. p. 332–9.

    Google Scholar 

  22. 22.

    Feldman A, Bauer AM, Castro-Herrera F, Chirio L, Das I, Doan TM, et al. The geography of snake reproductive mode: a global analysis of the evolution of snake viviparity. Glob Ecol Biogeogr. 2015;24(12):1433–42. https://doi.org/10.1111/geb.12374.

    Article  Google Scholar 

  23. 23.

    Lambert SM, Wiens JJ. Evolution of viviparity: a phylogenetic test of the cold-climate hypothesis in phrynosomatid lizards. Evolution. 2013;67(9):2614–30. https://doi.org/10.1111/evo.12130.

    Article  PubMed  Google Scholar 

  24. 24.

    Pincheira-Donoso D, Tregenza T, Witt WJ, Hodgson DJ. The evolution of viviparity opens opportunities for lizard radiation but drives into a climatic cul-de-sac. Glob Ecol Biogeogr. 2013;22(7):857–67. https://doi.org/10.1111/geb.12052.

    Article  Google Scholar 

  25. 25.

    Watson CM, Makowsky R, Bagley JC. Reproductive mode evolution in lizards revisited: updated analyses examining geographic, climatic and phylogenetic effects support the cold-climate hypothesis. J Evol Biol. 2014;27(12):2767–80. https://doi.org/10.1111/jeb.12536.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Tinkle DW. The distribution and evolution of viviparity in reptiles. Miscellaneous publications Museum of Zoology, University of Michigan, vol. 154; 1977. p. 1–55.

    Google Scholar 

  27. 27.

    Pyron RA, Burbrink FT. Contrasting models of parity-mode evolution in squamate reptiles. J Exp Zool B Mol Dev Evol. 2015;324(6):467–72. https://doi.org/10.1002/jez.b.22593.

    Article  PubMed  Google Scholar 

  28. 28.

    Takeuchi H, Takeuchi M, Hikida T. Extremely low genetic diversity in the Japanese population of Zootoca vivipara (Squamata: Lacertidae) revealed by mitochondrial DNA. Curr Herpetol. 2013;32(1):66–70. https://doi.org/10.5358/hsj.32.66.

    Article  Google Scholar 

  29. 29.

    Horreo JL, Peláez ML, Suárez T, Breedveld MC, Heulin B, Surget-Groba T, et al. Phylogeography, evolutionary history, and effects of glaciations in a species (Zootoca vivipara) inhabiting multiple biogeographic regions. J Biogeogr. 2018;45(7):1616–27. https://doi.org/10.1111/jbi.13349.

    Article  Google Scholar 

  30. 30.

    Shine R. “Costs” of reproduction in reptiles. Oecologia. 1980;46(1):92–100. https://doi.org/10.1007/BF00346972.

    Article  PubMed  Google Scholar 

  31. 31.

    Miles DB, Sinervo B, Frankino WA. Reproductive burden, locomotor performance, and the cost of reproduction in free ranging lizards. Evolution. 2000;54(4):1386–95. https://doi.org/10.1111/j.0014-3820.2000.tb00570.x.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Rodríguez-Díaz T, Braña F. Altitudinal variation in egg retention and rates of embryonic development in oviparous Zootoca vivipara fits predictions from the cold-climate model on the evolution of viviparity. Journal of Evolutionary Ecology. 2012;25:1877–87.

    Google Scholar 

  33. 33.

    Rodríguez-Díaz T, González F, Ji X, Braña F. Effects of incubation temperature on hatchling phenotypes in an oviparous lizard with prolonged egg retention: are the two main hypotheses no the evolution of viviparity compatible? Zoology. 2012;113:33–8.

    Article  Google Scholar 

  34. 34.

    Broennimann O, Fitzpatrick MC, Pearman PB, Petitpierre B, Pellissier L, Yoccoz NG, et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob Ecol Biogeogr. 2012;21(4):481–97. https://doi.org/10.1111/j.1466-8238.2011.00698.x.

    Article  Google Scholar 

  35. 35.

    Masó G. Effect of environmental predictability on life history traits and population dynamics. PhD Thesis. Barcelona: Universitat de Barcelona; 2019.

    Google Scholar 

  36. 36.

    Dupoué A, Blaimont P, Rozen-Rechels D, Richard M, Meylan S, Clobert J, et al. Water availability and temperature induce changes in oxidative status during pregnancy in a viviparous lizard. Funct Ecol. 2020;34(2):475–85. https://doi.org/10.1111/1365-2435.13481.

    Article  Google Scholar 

  37. 37.

    Cornetti L, Belluardo F, Ghielmi S, Giovine G, Ficetola GF, Bertorelle G, et al. Reproductive isolation between oviparous and viviparous lineages of the Eurasian common lizard Zootoca vivipara in a contact zone. Biol J Linn Soc. 2015;114(3):566–73. https://doi.org/10.1111/bij.12478.

    Article  Google Scholar 

  38. 38.

    Radder RS, Elphick MJ, Warner DA, Pike DA, Shine R. Reproductive modes in lizards: measuring fitness consequences of the duration of uterine retention of eggs. Funct Ecol. 2008;22(2):332–9. https://doi.org/10.1111/j.1365-2435.2007.01380.x.

    Article  Google Scholar 

  39. 39.

    Mclennan D, Recknagel H, Elmer KR, Monaghan P. Distinct telomere differences within a reproductively bimodal common lizard population. Funct Ecol. 2019;33(10):1917–27. https://doi.org/10.1111/1365-2435.13408.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Cornetti L, Ficetola GF, Hoban S, Vernesi C. Genetic and ecological data reveal species boundaries between viviparous and oviparous lizard lineages. Heredity. 2015;115(6):517–26. https://doi.org/10.1038/hdy.2015.54.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Berman DL, Bulakhova NA, Alfimov AV, Meshcheryakova E. N. How the most northern lizard, Zootoca vivipara, overwinters in Siberia. Polar Biol. 2016;39(12):2411–25. https://doi.org/10.1007/s00300-016-1916-z.

    Article  Google Scholar 

  42. 42.

    Grenot CJ, Garcin L, Dao J, Hérold JP, Hays B, Tséré-Pagès T. How does the European common lizard, Lacerta vivipara, survive the cold of winter? Comp Biochem Physiol A Mol Integr Physiol. 2000;127(1):71–80. https://doi.org/10.1016/S1095-6433(00)00236-1.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Breedveld MC, Fitze PS. A matter of time: delayed mate encounter postpones mating window initiation and reduces the strength of female choosiness. Behav Ecol Sociobiol. 2015;69(4):533–41. https://doi.org/10.1007/s00265-014-1864-y.

    Article  Google Scholar 

  44. 44.

    Fitze PS, Cote J, Clobert J. Mating order-dependent female mate choice in the poligynandrous common lizard Lacerta vivipara. Oecologia. 2010;162(2):331–41. https://doi.org/10.1007/s00442-009-1463-1.

    Article  PubMed  Google Scholar 

  45. 45.

    Breedveld MC, San Jose LM, Romero-Diaz C, Roldan ERS, Fitze PS. Mate availability affects the trade-off between producing one or multiple annual clutches. Anim Behav. 2017;123:43–51. https://doi.org/10.1016/j.anbehav.2016.10.025.

    Article  Google Scholar 

  46. 46.

    Fitze PS, Le Galliard JF. Inconsistency between different measures of sexual selection. Am Nat. 2011;178(2):256–68. https://doi.org/10.1086/660826.

    Article  PubMed  Google Scholar 

  47. 47.

    Le Galliard JF, Fitze PS, Cote J, Massot M, Clobert J. Female common lizards (Lacerta vivipara) do not adjust their sex-biased investment in relation to the adult sex ratio. J Evol Biol. 2005;18(6):1455–63. https://doi.org/10.1111/j.1420-9101.2005.00950.x.

    Article  PubMed  Google Scholar 

  48. 48.

    Fitze PS, Le Galiard JF, Federici P, Richard M, Clobert J. Conflict over multiple-partner mating between males and females of the polygynandrous common lizards. Evolution. 2005;59(11):2451–9. https://doi.org/10.1111/j.0014-3820.2005.tb00954.x.

    Article  PubMed  Google Scholar 

  49. 49.

    Shine R, Wapstra E, Olsson M. Seasonal shifts along the oviparity–viviparity continuum in a cold-climate lizard population. J Evol Biol. 2018;31(1):4–13. https://doi.org/10.1111/jeb.13202.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Acevedo P, Jiménez-Valverde A, Lobo JM, Real R. Delimiting the geographical background in species distribution modelling. J Biogeogr. 2012;39(8):1383–90. https://doi.org/10.1111/j.1365-2699.2012.02713.x.

    Article  Google Scholar 

  51. 51.

    Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model. 2011;222(11):1810–9. https://doi.org/10.1016/j.ecolmodel.2011.02.011.

    Article  Google Scholar 

  52. 52.

    Lobo JM, Jiménez-Valverde A, Hortal J. The uncertain nature of absences and their importance in species distribution modelling. Ecography. 2010;33(1):103–14. https://doi.org/10.1111/j.1600-0587.2009.06039.x.

    Article  Google Scholar 

  53. 53.

    Rödder D, Engler JO. Quantitative metrics of overlaps in Grinnellian niches: advances and possible drawbacks. Glob Ecol Biogeogr. 2011;20(6):915–27. https://doi.org/10.1111/j.1466-8238.2011.00659.x.

    Article  Google Scholar 

  54. 54.

    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25(15):1965–78. https://doi.org/10.1002/joc.1276.

    Article  Google Scholar 

  55. 55.

    Warren DL, Glor RE, Turelli M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution. 2008;62(11):2868–83. https://doi.org/10.1111/j.1558-5646.2008.00482.x.

    Article  PubMed  Google Scholar 

  56. 56.

    Broennimann O, Di Cola V, Guisan A. ecospat: Spatial Ecology Miscellaneous Methods. R package version 2.2.0. https://CRAN.R-project.org/package=ecospat; 2017.

    Google Scholar 

  57. 57.

    R Development Core Team. R: A language and environment for statistical computing: R foundation for statistical computing. Vienna, Austria. Available at https://www.R-project.org/; 2018.

    Google Scholar 

  58. 58.

    Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees: New York: Champman and hall/CRC; 1984.

    Google Scholar 

  59. 59.

    Therneau T, Atkinson B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1–13. http://CRAN.R-project.org/package=rpart; 2018.

    Google Scholar 

  60. 60.

    Therneau T, Atkinson B, Foundation M. An introduction to recursive partitioning using the RPART routines. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf; 2017.

    Google Scholar 

Download references

Acknowledgments

We would like to thank an anonymous referee who provided excellent comments on how to improve the manuscript.

Founding

J. L. Horreo was supported by a Spanish MINECO postdoc grant IJCI-2015–23,618. A. Jiménez-Valverde was supported by the Spanish Ramón y Cajal Program grant RYC-2013-14,441. Financial means were provided by Ministerio de Ciencias, Investigación, y Universidades (grant no. CGL2016–76918 AEI/FEDER, UE to P.S.F.). The founding bodies had no influence on the design, analysis, interpretation of the data, and writing of the manuscript. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

Data accessibility statement

Occurrence records are provided in Supporting Information Appendix S4 and Horreo et al. [29]. Climatic data are available from WorldClim database https://www.worldclim.org/ (Hijmans et al. [54]).

Author information

Affiliations

Authors

Contributions

JLH and PSF conceived the idea. All authors contributed to its design and the interpretation of the results. AJV analysed the data, with significant input from the rest of the authors. All authors wrote the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to P. S. Fitze.

Ethics declarations

Ethics approval

Not applicable, since all used coordinates and occurrence records stem from published articles.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix S1.- Climatic variables (Worldclim database [54]) used to test for differences in the climatic niche among parity modes. Used abbreviations and variable type (climatic averages, seasonal variability, and diurnal variability), as well as the time span for which the variables were used are given. For justification of their use see Appendix S2

Variable Abbreviation Variable type Time span of models
    annual reproductive period
mean diurnal temperature range BIO2 diurnal variability X X
isothermality* BIO3 diurnal variability X X
temperature seasonality (standard deviation)# BIO4 seasonal variability X X
maximum temperature of warmest month BIO5 average X  
minimum temperature of coldest month BIO6 average   X
mean temperature of warmest quarter BIO10 average X  
precipitation seasonality (standard deviation) BIO15 seasonal variability X X
minimum of the monthly maximum temperature minTmax average   X
mean of the monthly minimum temperature meanTmin average   X
  1. *Isothermality corresponds to BIO2/BIO7 (×100) and thus is a variance measure, with values close to 100 indicating that the average diurnal temperature range is the same as than the annual temperature range (BIO7 = BIO5-BIO6) and values smaller than 100 indicating that the average diurnal temperature range is smaller than the annual temperature range
  2. #temperature seasonality corresponds to the standard deviation of the monthly average temperature (× 100)

Appendix S2.- Correspondence and justification of the used climatic variables in the annual and reproductive period models

To obtain comparable results between annual and reproductive period analyses, variables which describe similar climatic patterns were used in both analyses (Appendix S1). BIO2, BIO3, BIO4, and BIO15 were used for the annual analyses as well as the reproductive period. BIO6, minTmax and meanTmin were used for the analyses based on the reproductive period. For the same reason BIO5 and BIO10 were used for the analyses based on the entire year. BIO6, minTmax, and meanTmin were not used for the annual analyses since on the Northern Hemisphere minimum temperatures during the coldest month and minimum of the monthly maximum temperature correspond to the hibernation period, which Z. vivipara spends in hibernacula where minimum temperatures are not lower than a few degrees below zero [41]. Thus, in the annual models BIO6 does not reflect the climatic conditions to which Z. vivipara is exposed. For several reasons, the remaining Bioclimatic variables were not included in any of the models: because they were not relevant for the climatic hypotheses explaining the evolution of viviparity given that they do not reflect the climatic conditions to which Z. vivipara is exposed (BIO1, BIO11, BIO12), because of the non-indpendency of certain variables (e.g. BIO7 = BIO5 – BIO6), and because it was not possible to calculate them for the reproductive period (BIO8, BIO9, BIO16-BIO19). Moreover, preliminary analyses including all 19 Bioclimatic variables showed that in our annual dataset the not used variables were redundant: the results including all 19 Bioclimatic variables were qualitatively the same as those presented here, namely, BIO4 was the response variable that decreased the impurty the most and oviparous populations exhibited smaller BIO4 (as in Fig. 8). Moreover, BIO2, BIO3, BIO15 were not of high importance in both annual datasets.

Appendix S3.- results of the annual analyses

Principal component analyses of the climatic variables including the entire year rendered two axes, which accounted for 75.4% of the variation (Fig. 6). The first axis explained 44.1% of the variation. It was positively related with the seasonality of temperature and precipitation (BIO4 and BIO15), and negatively with the other four variables (Fig. 7a). Temperature and precipitation seasonality (BIO4, BIO15) exhibited the lowest loadings, while the other variables exhibited medium loadings (Fig. 7a). The second axis, explained 31.3% of the variation (Fig. 6), and it mainly represents isothermality (BIO3) and temperature seasonality (BIO4; Fig.7b). On average, clade B occupies areas with the highest isothermality and the lowest temperature seasonality, whereas clade D inhabits areas with the lowest isothermality and highest temperature seasonality (Fig. 7c).

Niche similarity tests (Table 3) showed that only clades E and F exhibit significantly higher Dobs than expected by chance, and only clades E-F and A-C exhibit a high overlap (Dobs > 0.6 [34]). In 9 out of 15 comparisons, clades exhibited non-equivalent niches (Table 3).

Cross-validation points to a final tree of two leaves (one split) and a correct classification rate of 77.8% (Fig. 8). 40 out of 78 oviparous records (51.3%) and 104 out of 107 viviparous records (97.2%) were correctly predicted. 40 out of 41 occurrences of clade B (97.6%), 44 out of 47 occurrences of clade E (93.6%), and all occurrences of clade C, D and F were correctly classified. All occurrences of clade A were misclassified (Fig. 8). Variability related variables were the most influential factors (Fig. 9), namely temperature seasonality (BIO4) followed by isothermality (BIO3). Viviparous populations exhibited higher values than oviparous populations (Fig. 8), thus they inhabit areas with higher temperature seasonality and lower relative diurnal temperature variability (Fig. 9).

Appendix S4.- Zootoca vivipara clade, clade distribution (Fig. 3), sample name, and geographical coordinates of the 185 populations employed in this study

Clade Sample Longitude Latitude
A OI25 C 13.31005 46.54801
A OI13 A 13.22471 46.4583
A OI17A 12.75377 46.5768
A ZE7 16.4 46.416667
A ZZ5 14.216667 46.466667
A OI18 A 10.58579 45.73611
A OI8 B 11.09293 45.10046
A OI14 A 12.40341 46.08045
A OI7 A 13.02966 46.37538
A ZF50 12.5 46.25
A OI12 A 13.4835 46.55114
A ZF51 12.616667 46.4
A ZZ1–2 14.533333 46.533333
A OI34 A 11.79626 45.86797
A OI33 A 8.10722 45.92444
A HK1 14.966667 46.933333
A TV5–2 14.516667 47.016667
A OSL7 A 14.536 45.958
A OI32 A 11.31 45.9975
A HK3 14.916667 46.883333
A VS9 A 9.1206 46.15386
A Zu9 14.426426 47.018797
A OSL6 A 14.589 46.425
A ZZ3 14.766667 46.533333
A ZZ4–2 14.766667 46.533333
A OSL4 A 15.614 46.494
A ZU2–2 14.8 46.933333
A OI39 A 12.11666 46.78333
A ZF29 13 45.916667
A OI40 A 9.86666 45.98333
A ZE2 14.416667 45.583333
A ZF43 12.783333 46.316667
A ZU5 14.183333 46.75
A OI1 A 8.7 45.784
A OI27 A 9.41666 45.9
A OAU1 A 13.24721 46.70479
A ZZ2 14.566667 46.516667
A OSL5 A 13.758 46.491
B 691 −1.132169 43.044703
B 692 −1.132169 43.044703
B OF45A −0.66954 43.00468
B BUG1 −0.6383 43.1361
B 09–585 −3.700989 43.117228
B CS_1200 −0.549079 42.780883
B CS_1201 − 0.549079 42.780883
B CS_1202 −0.549079 42.780883
B CS_1203 −0.549079 42.780883
B CS_1204 −0.549079 42.780883
B PF_14_158 −0.397638 42.785724
B ETH2 −1.017 43.2437
B CS_527 −0.405052 42.795626
B CS_526 −0.405052 42.795626
B CS_531 −0.405052 42.795626
B OF33C −0.42331 42.89696
B PF136 −1.319452 43.021068
B OF25 −1.07265 43.04206
B AG09201 −1.80387 43.307327
B ISA 3 −0.797 43.031
B OF39 A −0.64748 42.90117
B Lizaso_014 −1.675353 42.960353
B OF41A −0.54698 42.80262
B OF31 B −0.40334 42.7947
B RC21 −1.314 43.023
B RHU1 −1.62798 43.31585
B Lizaso_016 −1.675353 42.960353
B Lizaso_017 −1.675353 42.960353
B Lizaso_018 −1.675353 42.960353
B OE8 B −6.914 42.866
B 09–503 −7.537261 43.466781
B 09–532 −6.230381 43.001131
B 09–505 −7.537261 43.466781
B PF-15-109 0.760853 42.627672
B PF-15-134 0.277289 42.677551
B OF36A −0.34547 42.97254
B BLA3 −0.5492 43.1162
B PF-15-098 0.997253 42.654031
B PF-15-100 0.997253 42.654031
B CAP1 −0.88997 44.21747
B CET1 −0.5806 42.9351
B PF-15-125 0.326901 42.94392
B PF-15-126 0.326901 42.94392
B PF_14_159 −0.397638 42.785724
B PF-15-001 2.076389 42.514553
B PF-15-003 2.076389 42.514553
B EST 1 −0.22923 42.9098
B HOU 1 −0.4357 42.9113
B PF-15-123 0.773036 42.9027
B Lizaso_015 −1.675353 42.960353
B OF29 A −1.294 43.506
B OF28A 0.158 43.055
B OF30A 1.980467 42.859806
B PF_14_234 0.954714 42.715994
B PF_14_236 0.954714 42.715994
B PF_14_228 0.954714 42.715994
B OF27A −0.727 44.916
B PF-15-075 1.675611 42.597092
B SS1 1 −0.396 42.8944
B PF-15-110 0.760853 42.627672
B PF-15-135 0.277289 42.677551
B PF-15-124 0.773036 42.9027
B PF-15-078 1.675611 42.597092
C ZL7–2 16.083333 47.533333
C VAU7 A 15.81342 47.65872
C ZX1 14.616667 47.566667
C ZN1 16.8 47.7
C NZ10 15.033333 47.733333
C NZ11 15.033333 47.733333
C NZ14 15.066667 47.083333
C ZL2–2 16.033333 47.5
C ZL3–2 16.033333 47.5
C ZM2 16.45 48.016667
C VD9–2 14.05 47.15
C NZ17 15.15 47.766667
C NZ18 15.15 47.766667
C VD3–2 14.2 47.4
C ZX5–2 14.95 47.516667
C ZX4 14.95 47.516667
C ZT4 13.583333 47.45
C ZL6–2 15.716667 47.716667
C ZL10 15.833333 47.783333
C VAU6 A 15.851 47.61803
C VD8 13.933333 47.316667
C ZL4 15.366667 47.383333
C ZL5–2 15.366667 47.383333
C VAU2 B 16.865 47.924
D ACLV14–26 24.478472 57.112853
D VR21A 87.25 51.8
D VRO1 B 22.333 46.197
D VRO4 A 24.951 47.474
D VRO4 B 24.951 47.474
D VH5 E 22.26628 47.76922
D VR32 B 38.389 56.013
D VU17 A 33.5 52.33
D VU18 A 24.229 47.901
D VRO2 A 24.917 46.66
D VU19 A 36.583 49.8
D VH8 A 22.50961 48.01079
D VBR1 A 23.909 53.911
D VRO5 D 23.197 45.655
D VR23 A 29.837 61.154
D F84 25.749483 62.175583
D VR38 B 89.26 51.469
D VR9A 30.349 59.451
D VRO3 A 22.346 47.314
D VU20 A 37.352 47.638
D FO86 25.82115 65.030733
D S26 22.836517 66.3806
D VR45 A 48.933 52.283
D VR26 B 33.823 66.338
D VR34 C 43.034722 56.039722
D VR16 A 99.999989 48.88
D VR39 A 41.458 53.19
D VR15 A 46.68 55.02
D VR27 A 28.768 58.434
D VR24 A 53.09 56.935
E VI26 A 13.2931 46.55386
E ZT2 13.95 47.766667
E VP3 18.2 59.266667
E VF27 A 3.855 49.905
E CB12 19.413 43.013
E VF20 A 6.16917 46.83505
E VSK3 A 22.087 48.452
E VF19 A 6.119 45.692
E vg2 6.55 51.05
E VS10 A 9.93056 46.78333
E ZJ1 12.9 48.083333
E VT2 A 22.65 48.583
E VU10 A 22.65 48.583
E VSU3 B 12.309 57.373
E VAU5 A 12.94962 46.61831
E ZV1–2 15.15 48.85
E ZV3 15.15 48.85
E ZX7 14.466667 47.683333
E ZX6 14.466667 47.683333
E VS7 A 8.483 46.733
E VBE9 B 13.87651 46.91384
E VU12 A 23.231 48.429
E VU11 A 23.241 48.839
E VSK4 A 22.274 48.962
E VYU2 F 20.685 43.461
E VP3 D 21.417 53.714
E VI19 A 13.14621 46.57559
E VF16 C 3.875 44.385
E VG1 5.832 51.759
E VU9 A 22.762 48.619
E VU14 A 22.762 48.619
E VB10 23.125 43.122
E SJ13 23.416667 41.066667
E VI18 A 13.27794 46.55907
E VT1 17.989 49.876
E VI42 A 7.039 44.88416
E VS5 A 6.183 46.533
E VSU2 A 16.687 56.703
E VR6 A 20.848 55.172
E ZF46 12.7 46.483333
E ZO1 12.6 47.216667
E VI39 A 12.95737 46.51414
E VP1 A 15.539 50.827
E VH2 B 22.53188 48.12641
E ZV4 15.066667 48.866667
E ZV5–2 15.066667 48.866667
E VI38 A 10.58777 46.26388
E VI44 A 10.95 46.21666
E VP2 B 22.654 49.098
E VI43 A 10.68333 46.48333
E VI21 A 11.76603 46.42007
E VP1 12.633333 51.316667
F WE-11 13.383333 46.866667
F WS11 12.466667 46.816667
F VAU4 D 13.14853 46.76571
F VAU4 A 13.14853 46.76571
F VH7 A 19.26977 47.23372
F VH7 C 19.26977 47.23372
F ZY1–2 14.916667 47.1
F ZY2 14.916667 47.1
F ZY4 15 47.016667
F VAU8 A 14.93428 46.78386
F VU3 19.35 46.8
F VU-2 19.35 46.8
F VU4–2 19.35 46.8
F ZV7 14.733333 46.933333
F ZU8 14.733333 46.933333
F VH1 14.95 46.48
F VH2 14.95 46.48
F WE12 13.25 46.983333
F VD1 14.15 47.1
F VD2 14.15 47.1
F VH4 D 19.22784 47.27035
F VH4 E 19.22784 47.27035
F ZU10 14.783333 46.95
F ZY3 15.283333 47.333333

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Horreo, J.L., Jiménez-Valverde, A. & Fitze, P.S. Climatic niche differences among Zootoca vivipara clades with different parity modes: implications for the evolution and maintenance of viviparity. Front Zool 18, 32 (2021). https://doi.org/10.1186/s12983-021-00403-2

Download citation

Keywords

  • Cold climate hypothesis
  • Parity mode evolution
  • Maternal manipulation hypothesis
  • Ecological niche
  • Oviparity
  • Viviparity