Philippa Kaur

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Data preparationHistorical dataThe primary source of data is historical parish registers, which have been transcribed under the supervision of many of the study authors over a number of decades, primarily for evolutionary demographic research. Our dataset (Supplementary Data 1) includes nine European populations, including some for which the positive relationship between maternal lifetime twinning status and maternal total births has been described previously5,6,8,10. Details for the populations used in this study are given in Table 1 and in Supplementary Table 14. The sourcing of each dataset and the socio-ecological background of each population have already been described in previous studies (see Table 1 for references). Overall, there is no reason to suspect a high level of consanguinity in these populations62, so our analyses do not account for the variable level of relatedness between individuals. The datasets cover pre-industrial periods in which the lifetime reproductive success was high and the majority of people were living and working in agrarian communities, except for the Samis (from northern Finland and Sweden) who made their living fully or in part from a combination of herding, fishing and hunting. The smooth decline of the probability of parity progression with parity (Fig. 4a) suggests that mothers did not effectively limit their reproductive success with the aim of achieving a small family size, as found in populations that have undergone a demographic transition.Data selectionWe use the term family to describe a mother and all individuals to whom she gave birth over her life. For our analyses, all families considered met the following criteria: the mother’s age was known at a monthly resolution and her life course traced until at least age 45 (approximating full reproductive life), the birth year and month of all offspring must have been recorded and consecutive births were all at least nine months apart from one another. In the case of one population (Norway) and of a few observations in the other populations, the month of birth was not available. These data were thus not considered in the results presented in main text because some of our analyses require an accurate estimation of the interbirth interval. Most analyses are thus based on data from eight populations. Nevertheless, the slope of the negative relationship between twinning and total birth remained very similar irrespectively of whether or not such data were included (Supplementary Fig. 5), which suggests that the exclusion of Norwegian data does not alter our main conclusions. Information on the populations considering also the data for which the birth months were missing is provided in Supplementary Table 14.Twin identificationIn our data, the maximum number of offspring to constitute a multiple birth was three. We use the term twin(s) to refer to offspring who were the result of the same multiple birth (including 1745 sets of twins sensu stricto and 19 sets of triplets in the filtered dataset and, respectively, 1915 and 20 in the dataset including observations that lack birth months). Although twins are sometimes explicitly indicated in the data sources, this is not always the case. Thus, for the sake of consistency across our populations, twin births were identified when at least two individuals born to the same mother appeared with similar birth dates, according to strict criteria: if the exact birth dates were available, then offspring were identified as twins if their birth dates were no more than one day apart. If the exact birth dates were not available, then an identical birth year and month were considered sufficient for positive twin identification.Analyses and simulationsCharacterisation of the relationship between twinning and fertilityWe began by characterising the relationship between lifetime twinning status and maternal total births by fitting two models. For the first, we used a Generalised Linear Mixed-effects Model (GLMM) to investigate whether the mothers of twins (twinners) had experienced a larger or smaller number of births than mothers who only had singletons (non-twinners). We fitted this GLMM on the mother-level data with the R package spaMM63 using the call:$$begin{array}{c}{{{rm{fitme}}}}left({{{{rm{births}}}}}_{{{{rm{total}}}}}sim 1+{{{rm{twinner}}}}+(1|{{{rm{pop}}}}),right.\ {{{rm{data}}}}={{{rm{mother}}}}_{{{rm{level}}}}_{{{rm{data}}}},\ left.{{{rm{family}}}}={{{rm{Tnegbin}}}}({{{rm{link}}}}={hbox{“}}{{{mathrm{log}}}}{hbox{”}})right)end{array}$$
(1)
The response variable births_total refers to all births recorded over a mother’s observed lifetime (count data). The term 1 informs the function to fit an intercept (which happens by default, but is indicated here for clarity). The predictor twinner refers to maternal lifetime twinning status (binary: twinner vs non-twinner) and is modelled as a fixed effect. The term pop refers to the population identity (qualitative variable with eight levels) and is modelled by a Gaussian random effect acting on the intercept, which allows for the modelling of the heterogeneity between populations that is not captured by the fixed effects. The argument family is used to define the error structure and the link function of the GLMM (more on this below).In a second model, we reversed which variable is used as a response and which is used as the fixed-effect predictor. This allowed us to analyse how maternal total births predicted the probability of a mother producing twins during her lifetime using the call:$$begin{array}{c}{{{{{rm{fitme}}}}}}left({{{{{rm{twinner}}}}}} sim 1+{{{{{rm{births}}}}}}_{{{{{rm{total}}}}}}+(1|{{{{{rm{pop}}}}}}),right.\ {{{{{rm{data}}}}}}={{{{{rm{mother}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ left.{{{{{rm{family}}}}}}={{{{{rm{binomial}}}}}}({{{{{rm{link}}}}}}={hbox{“}}{{{{{rm{logit}}}}}}{hbox{”}})right)end{array}$$
(2)
The fitted models 1 and 2 are depicted in Fig. 1a, b and Supplementary Tables 1,2. While models 1 and 2 represent two sides of the same coin, the fit of both models is justified because each model formulation provides complementary information: expressing the effect of twinning on fertility relates to previous work5,6,7,8,9,10,57 and expressing the effect of fertility on twinning is a first step toward identifying what shapes twinning propensity, the focus of this paper.For the model predicting total births (model 1), we chose to use a negative binomial error structure. Using this error structure produced a fit of the data that was better than a (truncated) Poisson—the usual alternative for count data—as evidenced by much smaller marginal and conditional AIC values64. Here we specifically used a truncated negative binomial distribution because the data do not possess zeros by construction (only mothers are present in the dataset, i.e. there are no nulliparous women). For the model predicting lifetime twinning status (model 2), we chose a binomial error structure which is appropriate for binary data.Modelling the proportion of twin births among all births per mother is an effective way to avoid biases caused by differences in exposure to the risk of having twins affecting the relationship between twinning and fertility. For this, we fitted the following third model:$$begin{array}{l}{{{{{rm{fitme}}}}}}left({{{{{rm{cbind}}}}}};({{{{{rm{twin}}}}}}_{{{{{rm{total}}}}}},{{{{{rm{singleton}}}}}}_{{{{{rm{total}}}}}})right.\ sim ,1+{{{{{rm{births}}}}}}_{{{{{rm{total}}}}}}+(1|{{{{{rm{pop}}}}}}),\ ,{{{{{rm{data}}}}}}={{{{{rm{mother}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ ,left.{{{{{rm{family}}}}}}={{{{{rm{binomial}}}}}};({{{{{rm{link}}}}}}={hbox{“}}{{{{{rm{logit}}}}}}{hbox{”}})right)end{array}$$
(3)
In this model, the variable twin_total refers to the mother’s total number of twin births (i.e. one for each twinning event), singleton_total refers to the lifetime number of singleton births, and the cbind() function serves to indicate the fitting function to model the frequency of twinning events based on these two variables, which is interpreted as number of successes and failures of a binomial experience. The fitted model 3 is depicted in Fig. 2 and Supplementary Table 3.We modified model 3 so as to test whether the effect of total births differed significantly between populations. To do so, we considered that the effect of populations on total births could either be modelled as an interaction between fixed effects or as a random slope. For the former representation, we thus compared the fit of a model with linear predictor structure defined in spaMM as 1+births_total*pop to that of a model with the structure 1+births_total+pop. For the latter representation, we compared the fit with linear predictor structure 1+births_total + (1|pop) (i.e. model 3 as introduced above) to that of 1+births_total + (1+births_total|pop). We performed this testing procedure by comparing the likelihood ratio between each pair of alternative fits to the expectation of such a ratio under the null hypothesis. The distribution of the statistics used for the test was computed using 1000 parametric bootstrap replicates, which we generated using the function anova() provided by spaMM63. The test revealed a small non-significant variation in slopes between populations (see Results). For the sake of simplicity, we thus considered the effect of births_total the same across populations in all other analyses.Modelling life-history events using GLMMsTo reveal the biological mechanisms responsible for the relationship between twinning and fertility, we first fitted statistical models describing how age, parity and twin/singleton status, as well as individual and population differences influenced three key life-history events: parity progression (PP), the duration of interbirth intervals (IBI) and the twinning outcome of births (T). These models were fitted on birth-level data by the following calls:$$begin{array}{l}{{{{{rm{fitme}}}}}}left({{{{{rm{PP}}}}}} sim 1+{{{{{rm{twin}}}}}}+{{{{{rm{poly}}}}}}({{{{{rm{cbind}}}}}}({{{{{rm{age}}}}}},,{{{{{rm{parity}}}}}}),,{{{{{rm{best}}}}}}_{{{{{rm{order}}}}}})right.\ ,+,(1|{{{{{rm{maternal}}}}}}_{{{{{rm{id}}}}}})+(1|{{{{{rm{pop}}}}}}),\ ,{{{{{rm{data}}}}}}={{{{{rm{birth}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ ,{{{{{rm{family}}}}}}={{{{{rm{binomial}}}}}};({{{{{rm{link}}}}}}={hbox{“}}{{{{{rm{logit}}}}}}{hbox{”}})end{array}$$
(4)
$$begin{array}{l}{{{{{rm{fitme}}}}}}left({{{{{rm{IBI}}}}}} sim 1+{{{{{rm{twin}}}}}}+{{{{{rm{poly}}}}}};({{{{{rm{cbind}}}}}}({{{{{rm{age}}}}}},,{{{{{rm{parity}}}}}}),{{{{{rm{best}}}}}}_{{{{{rm{order}}}}}})right.\ ,+,(1|{{{{{rm{maternal}}}}}}_{{{{{rm{id}}}}}})+(1|{{{{{rm{pop}}}}}}),\ ,{{{{{rm{data}}}}}}={{{{{rm{birth}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ ,left.{{{{{rm{family}}}}}}={{{{{rm{negbin}}}}}}({{{{{rm{link}}}}}}={hbox{“}}{rm log} {hbox{”}})right)end{array}$$
(5)
$$begin{array}{l}{{{{{rm{fitme}}}}}}left({{{{{rm{T}}}}}} sim 1+{{{{{rm{poly}}}}}}({{{{{rm{cbind}}}}}}({{{{{rm{age}}}}}},,{{{{{rm{parity}}}}}}),,{{{{{rm{best}}}}}}_{{{{{rm{order}}}}}})right.\ ,+,(1|{{{{{rm{maternal}}}}}}_{{{{{rm{id}}}}}})+(1|{{{{{rm{pop}}}}}}),\ ,{{{{{rm{data}}}}}}={{{{{rm{birth}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ ,left.{{{{{rm{family}}}}}}={{{{{rm{binomial}}}}}};({{{{{rm{link}}}}}}={hbox{“}}{{{{{rm{logit}}}}}}{hbox{”}})right).end{array}$$
(6)
The response variables of models 4, 5 and 6 are thus PP, IBI and T, which refer to whether the mother went on to reproduce again or not (a boolean), the duration of the interbirth interval between the focal birth and the next (a discrete number of months) and whether the birth resulted in twins or not (a boolean), respectively. In addition to the terms that have already been defined, we now have the term poly(cbind(age, parity), best_order) to code for a polynomial describing the effect of maternal age, parity and their possible interaction. The two-variable polynomial function was applied on maternal age (with a monthly resolution) and parity (i.e. the current birth rank). Such a polynomial term allowed us to explore the influences of maternal age and parity on each response variable while encompassing the non-linearity of these predictors. We also have the predictor variable twin, which is a boolean that indicates if the previous birth event of a given mother resulted in twins or not (the variable twin and T are the same, but we used two different names to clarify when it is used as a response or as a predictor). Finally, we have the random effect “maternal identity” (maternal_id), which is used to represent intrinsic variation among mothers, that is, heterogeneity of expected response among individuals, beyond that due to the fixed effects and the population random effect. This random effect therefore measures maternal intrinsic fertility (in models 4 & 5) and twinning propensity (in model 6).To determine the best polynomial order (best_order) for the polynomial term we attempted orders from 0 to 6 and selected, for each model, the order leading to the model fit associated with the smallest marginal AIC. A polynomial of order 6 is sufficient to fit very complex shapes. Polynomial orders obtained by this procedure are given in the summary tables of the model fits given in Supplementary Tables. Importantly, maternal age and parity are highly correlated together (Spearman’s rho = 0.69), unequally correlated to response variables and exert non-linear effects. These are precisely the conditions in which collinearity issues are the most severe65. This justifies why we considered them jointly in all statistical models, as well as why we did not attempt to partition their respective biological effects in our analyses (except for the visual representation in Fig. 4).In order to study how the lifetime twinning status influenced maternal age at first birth, we also fitted the following model:$$begin{array}{l}{{{{{rm{fitme}}}}}}left({{{{{rm{AFB}}}}}} sim 1+{{{{{rm{twinner}}}}}}* {{{{{rm{births}}}}}}_{{{{{rm{total}}}}}}_{{{{{rm{fac}}}}}}+(1|{{{{{rm{pop}}}}}}),right.\ ,{{{{{rm{data}}}}}}={{{{{rm{mother}}}}}}_{{{{{rm{level}}}}}}_{{{{{rm{data}}}}}},\ ,left.{{{{{rm{family}}}}}}={{{{{rm{negbin}}}}}}({{{{{rm{link}}}}}}={hbox{“}}{rm log} {hbox{”}})right)end{array}$$
(7)
In this model, the response variable AFB corresponds to the age at first birth expressed as a number of months (discrete data) and the predictor births_total_fac corresponds to a qualitative variable referring to maternal total births (10 levels: 1, 2, …, 9, 10 + ). We here considered a possible interaction between twinner and births_total_fac. We used the negative binomial family as in model 1 but as for model 5 there is no need to consider here the truncated form of the distribution. All other terms have already been defined. The fitted model is depicted in Supplementary Fig. 1 and Supplementary Table 7.Marginal predictions for GLMMsAll predictions shown in plots or given in text represent marginal predictions. This means that the predictions for the quantities of interest (maternal lifetime births, twinning probabilities and age at first birth) are a function of coefficients of the fixed effects, and of the variance of the random effects. To be more precise, we averaged, over the fitted distribution of random effects, the predictions expressed on the scale of the response (i.e. back-transformed from the scale of the linear predictor) and conditional on the fixed and random effects. Unlike the traditional conditional predictions computed for a specific value of the random effects (often 0), such computation provides unbiased predictions and should be favoured in the context of GLMMs where random effects act non-additively on the expected response (which is the case when the link function of the model is not identity66). We estimated 95% intervals for these marginal predictions (CI95%) using parametric bootstraps with the help of the function spaMM_boot() from the R package spaMM and boot.ci() from the R package boot. More details can be found by looking at the code of the functions compute_predictions() and compare_prediction() in our supporting R package twinR (see Code availability).Simulating the life history of mothersWe produced an individual-based simulation model of human female life history to investigate the contribution of four mechanisms to the relationship, shown in Fig. 2, between per-birth twinning probability and maternal total births—an approach generally known as pattern-oriented modelling67. Each simulation proceeds in the following way: first, we initialised the simulation with representations of the exact same mothers present in the observed dataset, setting their population and maternal identities as the real ones, their starting ages at the observed values for age at first birth and their parity to one. Following this initialisation, the virtual lives of mothers proceeded as multiple iterations of a sequence of three life-history events, informed by statistical models (see below) and subject to the hypothesis being tested (Supplementary Figs. 3, 4). Specifically, for each mother, the twin/singleton status (T) of the current birth was first determined using a GLMM predicting T. Then, whether or not she will go on to reproduce at least once more was determined by simulating her parity progression status (PP) using a GLMM predicting the parity progression probability. For mothers who do continue reproducing, we finally used a third GLMM to determine the length of the interval between the current birth and the next one (IBI). At each iteration, a mother’s parity is increased by one, and age is increased by the simulated length of the interbirth interval. All predictions were performed conditionally on the value for the predictor characterised by both fixed and random effects. The process of simulating PP, IBI and T was then reiterated until all women had ceased reproduction, which happens necessarily since the probability of parity progression is lower than one. We also set this probability to zero once women reached 60 years old to save computation time in particular simulation scenarios leading to unrealistic life histories (and bad goodness of fit). Note that the maximum recorded age at which a mother gave birth was 55.1 years in our data. For the same reason, we also capped the maximum simulated duration for the interbirth interval to 30 years.Drawing life-history events from the fit of the model formulas shown above for models 4, 5 and 6 corresponds to simulating the scenario PISH (i.e. all four hypothetical mechanisms are activated). For simulating other simulation scenarios, we had to fit additional GLMMs derived from models presented above. Specifically, the term twin was dropped from model 4 to deactivate mechanism P (model 8; Supplementary Table 8); the term twin was dropped from model 5 to deactivate mechanism I (model 9; Supplementary Table 9); the term poly(cbind(age, parity), best_order) was dropped from model 6 to deactivate the mechanism S (model 10 and 11; Supplementary Tables 10, 11); and the term (1|maternal_id) was dropped from model 6 to deactivate mechanism H (model 11 and 12; Supplementary Tables 11, 12).Testing candidate mechanisms using simulationsTo test how each mechanism or association of mechanisms influenced the relationship between twinning and fertility, we ran simulations under each possible set of activated or inactivated mechanisms. We tested all possible sets and we thus built a total of 42 = 16 simulation scenarios (Supplementary Figs. 3, 4).For each simulation scenario, we ran simulation replicates (see Supplementary Notes for details and information on the numbers of replicates), then fitted model 3 on the dataset produced by each replicate and extracted the estimate for the slope associated with the term births_total in that model (β*). We then consider, in turn, that each simulation scenario may have generated the data. Each simulation scenario is thus considered as a null hypothesis which we aim at testing. Such a test is traditionally referred to as a goodness-of-fit test68. The result of such a test, a p-value, answers the question: what is the probability of obtaining a value equal to, or more extreme than, the statistic of interest, if the null hypothesis were true? The rejection of the null hypothesis by the test (i.e. a p-value ≤ 0.05) signifies a rejection of the null hypothesis, and thus, here, the rejection of a simulation scenario which represents a particular mechanism, or combination of mechanisms. In contrast, a large (i.e. non-significant) p-value would here denote support for the simulation scenario under consideration.A first candidate, as a statistic of interest to build our goodness-of-fit test, is the slope β*. However, when viewed as a goodness-of-fit test, the direct comparison of the observed and simulated slopes may be conservative when other life-history parameters are fitted to the data. This is because the data tend to be more likely given parameter values fitted to the data than given the actual (unknown) parameter values that generated the data. The goodness-of-fit test is however only guaranteed to provide uniformly distributed p-values (a feature necessary for the correctness of any null hypothesis testing) when samples are drawn under the latter parameter values. This is a general issue in statistics which has also been discussed long ago, for example, when the data-generating process is the normal distribution and a Kolmogorov–Smirnov test of goodness-of-fit is applied69. We thus designed and validated a specific procedure to correct for such bias while testing each simulation scenario (Supplementary Notes). In the text, we only report outcomes from this unbiased goodness-of-fit test (for details, see Supplementary Notes and Supplementary Table 13).Studying the effect of twinning propensity on the number of offspring using simulationsTo study how twinning propensity influences the total number of offspring that mothers produced during their lifetime, we ran two sets of simulations, each with 100 replicates. In the first set, we ran the simulation as described in the section “Simulating the life history of mothers” using the fits of the models associated with the simulation scenario PIS (i.e. fits of models 4, 5 and 12). In the second set, we did the same, except that we modified the intercept of the model predicting twinning events (fit of the model 12) by adding 2.5 to its intercept. We also tried other values, some smaller (e.g. 0.25), some larger (e.g. 5), to make sure that the magnitudes of the change of the intercept did not impact our qualitative statements. For each set of replicates, we extracted the twinning rate, the twinner rate, the mean number of offspring produced and the mean total number of births. We report the means of these metrics, as well as the 95% Central Range from simulation replicates (CR95%), which we directly computed by extracting the corresponding quantiles from the distribution generated by the replicates.Realism of the simulationsWe checked that the simulated life history closely matched that of the real mothers represented in our dataset beyond what is captured by the relationship between twinning and fertility. To do so, we compared different metrics related to fertility and twinning between the real and simulated data. We chose to perform this comparison under the simulation scenario PIS since it produces the best goodness-of-fit. The results of this quality check confirm that our simulations represent the reproductive lives of the mothers appropriately (Supplementary Fig. 6).Studying the effect of mortality on the number of offspring using simulationsTo account for the fact that not all offspring have the same expected survival, we also applied a survival weight to each simulated offspring before averaging the numbers for a given simulation set (baseline twinning propensity or enhanced, see Results). We used as weights the estimates for the probability of offspring survival between birth and adulthood provided by two publications associated with some of the data we used there. Specifically, following Helle et al.8, we used a weight of 0.603 for twins, 0.838 for singletons from twinners and 0.815 for singletons from non-twinners. Alternatively, following Haukioja et al.3, we used a weight of 0.337 for twins and 0.706 for singletons from all mothers.Implementation detailsAll statistical analyses were performed in R version 4.170. The main R packages we used were spaMM63 version 3.9.40 for the fit of all the statistical models, boot71,72 version 1.3-28 for the computation of confidence intervals based on parametric bootstraps, and R673 version 2.5.1 for defining the object used to run the simulations. The DESCRIPTION file from our package twinR (see Code availability) lists the additional R packages required for this project (e.g. those used for plotting and data manipulation).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

None of the 21 targets of the United Nations’ Sustainable Development Goals (SDGs) set for 2020 was achieved. But, by our calculations, the target to protect 10% of the global ocean area (SDG14, target 5) could become a reality this year.
Competing Interests
The authors declare no competing interests. More

Ethics statementThe data collection and experiments were conducted in accordance with the collecting permits (SGPA/DGVS/07946/08, 03369/12, 00228/13, 07587/13, 01629/16, 01205/17, 02490/17, 06768/17, 000998/18, 002463/18, 002490/18, 002491/18, 003209/18, and 02523/19) approved by Dirección General de Vida Silvestre, México.Phylogeny and divergence time estimationTo estimate the phylogeny and divergence time among phrynosomatid species we used sequences of five mitochondrial and eight nuclear genes available in GenBank for 149 taxa (Supplementary Data 2). Accession numbers were the same as those used in Martínez-Méndez et al.58 for the Sceloporus torquatus, S. poinsettii and S. megalepidurus groups and the same as those in Wiens et al.59 for other phrynosomatid species. For taxa not included in the previous references, we searched GenBank for available sequences. We then performed alignments for each gene using MAFFT (ver. 7)60 and concatenation and manual refinement using Mesquite (ver. 3.6);61 obtaining a concatenated matrix of 9837 bp for 149 taxa (Supplementary Data 3). For the relaxed clock analyses, three nodes were calibrated using lognormal distributions based on two previous studies59,62. The first calibration was set for the Sceloporus clade (offset 15.97 million years ago (MYA)) based on a fossil Sceloporus specimen63). The second calibration point was set for the Phrynosoma clade (offset 33.3 MYA) based on the fossil Paraphrynosoma greeni64, and the last calibration point was for the Holbrookia-Cophosaurus stem group (offset 15.97 MYA) given the fossil Holbrookia antiqua63. We conducted dating analysis with the concatenated sequences matrix, partitioned the mitochondrial and nuclear information, each gene under GTR + I + Γ model, and allowed independent parameter estimation. We performed Bayesian age estimation with the uncorrelated lognormal relaxed clock (UCLN) model in BEAST (ver. 2.5.2)65,66 and run on CIPRES67. Tree prior (evolutionary model) was under the Birth-Death model, and we ran two MCMC analyses for 100 million generations each and stored every 20,000 generations. We assessed the convergence and stationarity of chains from the posterior distribution using Tracer (ver. 1.7)68. We combined independent runs using LogCombiner (ver. 2.5.2; BEAST distribution)69 and discarded 30% of samples as burn-in, obtaining values of effective sample size (ESS) greater than 200. We estimated the maximum clade credibility tree from all post-burnin trees using TreeAnnotator (ver. 1.8.4)69. The ultrametric tree is available as Supplementary Data 4. As we describe below, we accounted for phylogenetic uncertainty in our models by reperforming analyses using 500 trees that we randomly sampled from our posterior distribution. The 500 sampled trees are available as Supplementary Data 5.Data collectionParity modeWe categorized each species as either oviparous or viviparous based on previously published databases21,37,51,70, published references, and unpublished data (Supplementary Data 1). Our assignations align with other studies, except for one species, Sceloporus goldmani, which has been previously considered a viviparous species21,71,72,73. The only available sequence in GenBank (U88290) for that species is from a male (MZFC-05458) collected in Coahuila, Mexico72. However, in that same locality, one of us (F. R. Méndez-de la Cruz; unpubl. data) collected two females of the same species, and both laid eggs. Thus, the population of S. goldmani herein included is considered oviparous. Considering S. goldmani viviparous increases the number of originations of viviparity to 6 (from 5) in this lineage (Supplementary Fig. 4), but does not alter the outcome of our model-fitting analyses of trait evolution (Supplementary Table 7).Thermal physiologyWe compiled a database of four thermal physiological traits that influence the performance and fitness of ectotherms74 for 104 phrynosomatid species. These data were gathered from both published sources and from our own field and laboratory work (Supplementary Data 1). The thermal physiological traits we examined were the field body temperature (Tb) of active lizards, the preferred body temperature (Tpref) in a laboratory thermal gradient75, cold tolerance (critical thermal minimum, CTmin), and heat tolerance (critical thermal maximum, CTmax). These latter two traits (CTmin and CTmax) describe the thermal limits of locomotion; specifically, they describe the lower and upper temperatures, respectively, at which lizards fail to right themselves when flipped onto their backs55,76. To minimize the confounding effects of experimental design, we limited our data selection to species that were measured with similar methods. Correspondingly, our new data collection approach mirrored that of the published studies from which we extracted data. To obtain mean values for each thermal physiological trait (CTmin, Tb, Tpref, and CTmax) we did not mix data measured from different locations (instead, we used data from the population with the highest sample size).For species that we newly measured thermal physiological traits, we obtained the data as we describe below, and we based our methodology on the previous work55,56,75,76. We captured active (perching) adult lizards by lasso or by hand, and immediately ( More

Source and operation-wise energy utilization patternField operations/seedbed preparationEnergy used in different field operations under various crop management activities was significantly affected by the rice establishment methods and was ranged from 422 to 436 MJ ha−1 (Table 1 and Fig. 1, S2). Business as usual (Sc1) with high energy intensive practices consumed the highest (4336 MJ ha−1) energy in seed bed preparation, whereas in Sc5 and Sc6 no energy was required for seed bed preparation (Fig. 1). CSAP (mean of Sc4, Sc5 and Sc6) consumed 57% less energy in crop establishment (transplanting/sowing) operations compared Sc1 (978 MJ ha−1). Irrespective of field operations, tillage consumed highest input energy in conventional management practice of RW system. This was due to repeated (5–6 passes) dry and wet tillage to prepare a seedbed for nursery raising and puddling consumed more diesel in machinery in Sc1. In addition to this, Sc1 and Sc2 required 15–20 additional manual labour for transplanting rice seedlings.Table 1 Energy (MJ ha−1) utilization pattern under different management practices in rice and wheat (mean of 3-years).Full size tableFigure 1Operation-wise input energy-use pattern (%)under different management practices in rice. Where; Sc1, business as usual-conventional tillage (CT) without residue; Sc2, CT with residue; Sc3, reduce tillage (RT) with residue + recommended dose of fertilizer (RDF); Sc4, RT/Zero tillage (ZT) with residue + RDF; Sc5, ZT with residue + RDF + GreenSeeker + Tensiometer; Sc6, Sc5 + Nutrient expert.Full size imageIn wheat, energy used under different management practices for seedbed preparations ranged from 892 to 3078 MJ ha−1 and were significantly affected by crop establishment method (Table 1). In seedbed preparation, Sc1 and Sc2 consumed highest energy (2228 MJ ha−1) followed by Sc3 (1382 MJ ha−1), whereas in Sc5 and Sc6 no energy was required for seed bed preparation. Sc3-Sc6 consumed ~ 53% less energy in seedbed preparation and in sowing compared to Sc1 (Fig. 2). Business as usual (Sc1) consumed more energy because of it required more tillage operations in seedbed preparation1,4. However, in CSAP, tillage is not required for seeded preparation and energy is used only for seed sowing.Figure 2Operation-wise input energy-use pattern (%) under different management practices in wheat. Where; Sc1, business as usual or conventional tillage (CT) without residue; Sc2, CT with residue; Sc3, reduce tillage (RT) with residue + recommended dose of fertilizer (RDF); Sc4, RT/Zero tillage (ZT) with residue + RDF; Sc5, ZT with residue + RDF + GreenSeeker + Tensiometer; Sc6, Sc5 + Nutrient expert.Full size imageOn the system basis, CSAP consumed 76% less energy in seed bed preparation compared to Sc1 (7416 MJ ha−1) (Fig. 3). The higher energy consumption in tillage could be due to fewer usages of modern agricultural machineries and higher use of human & animal power in conventional RW production (Fig. 3). These findings are in support of many other researchers they revealed that diesel consumption (15–20 L ha−1) can be reduced by minimizing numbers of tillage operations5,6. Gathala et al.9 and Laik et al.11 have also described that more tillage operations are the biggest energy consumer (~ 40% of the total energy) compared to best agronomic management practices.Figure 3Operation-wise input energy-use (%) of RW system under different management practices. Where; SFPI are seed, fertilizer, pesticides and irrigation. Sc1, business as usual-conventional tillage (CT) without residue; Sc2, CT with residue; Sc3, REDUCE tillage (RT) with residue + recommended dose of fertilizer (RDF); Sc4, RT/Zero tillage (ZT) with residue + RDF; Sc5, ZT with residue + RDF + GreenSeeker + Tensiometer; Sc6, Sc5 + Nutrient expert. Vertical bars indicate ± S.E. of mean of the observed values.Full size imageSeed, fertilizers, pesticides and irrigation (SFPI)In rice production, agronomic energy inputs (SFPI) consumed ~ 84% of the total energy inputs, of which irrigation alone consumed about 46% (mean of six scenarios’ total energy input 3,8483 MJ ha−1) (Table 1 and Fig. 1, S2). Sc1 (puddled transplanted rice; PTR) consumed 29% higher energy in irrigation compared to CSAP (direct seeded rice; DSR) (Fig. 1). This was due to more electricity consumption in lifting of irrigation water from borewell for nursery raising, puddling operations and continuous flooding of water to complete the life cycle of crops. Furthermore, inorganic fertilizers were the second most important input that accounted for ~ 36% of total energy. Chaudhary et al.4and Pathak et al.15 stated that out of the total energy, about 43% energy is required for irrigation and fertilizers in rice production. The CSAP consumed 76, 22 and 11% less energy in pesticides, irrigation and fertilizer, respectively compared to Sc1 (Fig. 1). However, the seed energy was lower in Sc1 (transplanting methods) of rice production than CSAP (DSR), since the seed rate was used lower in PTR; these results were in accordance with Chaudhary et al.4 and Yuan et al.12. Similarly, CSAP (DSR) recorded 87% more energy for weed control and inter-cultivations than Sc1 (PTR), due to use of higher amount of herbicides in DSR (Sc3–Sc6). While in PTR (Sc1 and Sc2), submergence of water minimized the weed problem, which contributed to lesser use of herbicides. Nevertheless, the energy savings in various interculture operations and weed management practices under PTR weren’t enough to compensate its more energy consumption in nursery raising, puddling for rice seedling transplantation and irrigation. Overall, Sc6, Sc5, Sc4 and Sc3 consumed 23, 20, 18 and 15% less energy in SFPI compared to Sc1 (37,212 MJ ha−1) (Fig. 1). Laik et al.11 and Nassiri et al.16 results are validated by those who reported the highest energy consumption in conventional RW production system compared to CA based RW system.Like rice, in wheat production also, agronomic energy inputs/SFPI were the major energy consumers that contributed nearly 84% energy out of the total energy (21,660 MJ ha−1) (Table 1). Among the agronomic inputs (SFPI), fertilizer (F) was the foremost energy input requiring about 70% energy (18,208 MJha−1) of the total energy. Furthermore, irrigation is the second major energy consumer that contributed around 16% of the total agronomic energy inputs (Table 1 and Fig. 2). Overall, CSAP consumed 18.2 and 17.6% lesser energy in fertilizer and irrigation respectively, compared to Sc1 (14,328 and 3928 MJ ha−1) (Fig. 2). Less fertilizer and irrigation requirement under CSAP was due to precision agronomic input management, whereas, in Sc1 more use of N fertilizer and irrigation was made it more energy intensive. However, CSAPs consumed 26% higher energy in pesticides than to Sc1 (364 MJ ha−1). Sc6, Sc5, Sc4 and Sc3 consumed 20, 17, 11 and 8% less energy under SFPI compared to Sc1 (20,090 MJ ha−1). The findings of the present study are in accordance with some researchers12. On the system basis, CSAP consumed 19% lower energy under agronomic inputs/SFPI compared to Sc1 (57,485 MJ ha−1) (Fig. 3).Crop managements, harvesting and threshingThe energy utilization pattern for rice production in different crop management operations (intercultural, weeding and inputs application) are presented in Table 1 and Fig. S2. In 3-years, CSAP consumed 23% less energy under various crop management activities compared to Sc1 (2394 MJ ha−1). Among the crop management practices, CSAP consumed 33% higher energy in weeding operation compared to Sc1 in rice production (Fig. 1). Likewise, in wheat production, Sc6 and Sc5 computed 19% less energy in crop management activities compared to Sc1 (487 MJ ha−1). Sc1, Sc2 and Sc3 consumed 15.6 MJ ha−1 higher energy in weeding operations whereas, no energy required in weeding under CSAP (mean of Sc4, Sc5 and Sc6) (Table 1). The similar energy use pattern was recorded under all scenarios for harvesting and threshing operations in both the crops (Fig. 2). In RW system, CSAP and Sc3 consumed 23 and 13% less energy in input application compared to Sc1 (2264 MJ ha−1), respectively (Fig. 3). The highest energy use in various crop management practices under Sc1 was due to more energy required for the application of fertilizers, pesticides, hand weeding and inter-culture operations compared to CSAP. Findings of current study are in accordance who also recorded that smart crop management practices required less energy compared to conventional practices4,5,12,17.Direct–indirect and renewable–non-renewable energyIn rice production, direct and non-renewable energy consumption was more than indirect and renewable energy (Table 2). Direct energy in different cultivation methods of rice was in the range of 57–63%, whereas indirect energy was 37–43% of total energy consumed. Among the direct energy sources, application of irrigation water in all scenarios of rice cultivation consumed the highest direct energy, which showed that irrigation methods in rice cultivation should be standardized with low water use for its future sustainability. The findings of past researchers highlighted that more tillage operation before planting needed around 1/3rd of the total field operational energy, and that can be saved without affecting the crop yields with the adoption of zero tillage based rice cultivation practices6,9,15,18. CSAP (mean of Sc4, Sc5 and Sc6) recorded 43 and 17% less consumption of direct energy & indirect energy in rice cultivation compared to Sc1 (19,264 and 5735 MJ ha−1), respectively. The Sc3 also recorded 20 and 17% less consumption of direct & indirect energy compared to Sc1, respectively (Table 2). The contrast effects (BAU vs CSAP and I-BAU vs CSAP) were significant for direct and indirect energy (Table S2). However, BAU versus I-BAU was not-significant for direct energy but significant for indirect energy.Table 2 Total energy input (MJ ha−1) in the form of direct, indirect, renewable and non-renewable energy for different management practices under the rice, wheat and RW system.Full size tableThe contribution of renewable energy was very low in rice cultivation methods and it highlighted that the cultivation of rice is mainly based on non-renewable sources4,5,11,15. In our study, higher percent of electricitical energy consumed for water pumping from tube-wells, could be owing to less charges of electricity in Haryana, India19,20,21. In the study`s area, electric energy consumed in crop production is generated mostly from non-renewable sources, particularly fossil fuels. Furthermore, non-renewable sources are still the main fuel in power plants. The contrast effect (BAU vs CSAP) was significant for renewable and non-renewable energy (Table S2).In wheat cultivation methods, indirect & non-renewable energy consumption was greater than the direct & renewable energy. Less renewable energy uses in wheat cultivation showed that wheat production is mainly based on non-renewable resources. CSAP recorded 52 and 19% less direct and indirect energy in wheat cultivation compared to Sc1, respectively (Table 2).In RW system, direct and indirect energy consumption varied from 24,999 to 37,452 MJ ha−1 and 26,068 to 33,087 MJ ha−1, respectively (Table 2). Business as usual required more direct energy (diesel in field operations, electricity in irrigation and labour in crop management) than indirect energy in the CT-based RW system. However, CSAP required less direct energy compared to indirect energy, which showed that less number of field operations are required under CSA-based RW production system. The contrast effect (BAU vs CSAP) were significant to direct and indirect energy (Table S2).In RW system, higher renewable & non-renewable input energy was recorded under Sc1 and Sc2 (4582 and 65,957 MJ ha−1) followed by Sc3 (4306 and 54,906 MJ ha−1) as compared to CSAP (3985 and 47,082 MJ ha−1) (Table 2). The contrast effects were significant to renewable & non-renewable energy (Table S2). Present study indicated that conventional RW production system in the IGP plains are mostly dependent on non-renewable energysources4,15,20,22. Overall, non-renewable energy through fuel, electricity for ground water, inorganic fertilizers, pesticides and farm machineries shared maximum energy inputs followed by renewable resources viz.,labour, tractor, seed, etc.11,15,18. Dependence on non-renewable energy impacted the sustainability of the RW system15. Noteworthy, renewable energy is eco-friendly as well as reliable source of energy; hence, the use of renewable energy highlighted huge benefits, counting lesser contributions to greenhouse gasses emissions and enhanced environmental quality5. The present findings highlighted that more focus should be kept to improve, renewable energy use, technical innovation and optimized investment in rice and wheat production.Energy balance sheet (input–output and net energy)The total energy used for various rice production methods varied from 32,606 to 45,685 MJ ha−1 and was significantly affected by different crop management practices (Table 3). Our study results are in track with those of other similar research studies conducted in the IGP region for RW system4,5,11. Among the different rice production methods, PTR cultivation method (Sc1) of rice noted higher energy input than the CSAP (DSR method). Sc1 (32,606 MJ ha−1) recorded 40, 35, 27 and 23% higher energy use in rice production over Sc6, Sc5, Sc4 and Sc3, respectively (Table 3). Similarly, Sc1 (32,606 MJ ha−1) recorded 35, 29, 22 and 13% higher energy use in wheat production over Sc6, Sc5, Sc4 and Sc3, respectively. The CSAP and Sc3 used 24 and 16% less energy under RW system compared to Sc1 (70,538 MJ ha−1), respectively. However, CSAP recorded higher energy output from rice, wheat and RW system compared to Sc1. Compared to Sc1, the CSAP produced 1, 14 and 6% higher grain output energy under rice, wheat and system, respectively. The minimum input and maximum output energy under Sc6 were due to gained more net energy for both the crops during the respective years (Table 3). Linear contrast effects were significant to total energy input in rice, wheat and RW production systems. However, contrast effects were not significant to energy input in rice and RW system but significant to wheat production system.Table 3 Energy (MJ ha−1) balance under different management practices in rice, wheat and RW system (mean of 3 years).Full size tableIn rice production, the energy saving under CSAP was due to less energy inputs used in electricity that was associated with less irrigation water use in cultivation4,5,11. Efficient water management practice had a positive effect on energy consumption5,11and diverse energy sources across water regimens in India1,12,16. Our study showed that the energy input in existing rice and wheat production can be further minimized with precision water management techniques and, optimization of irrigation water management based on the precision land-levelling, frequent irrigation in rice, tensiometer based irrigation and zero tillage can efficiently decrease the total energy consumption in the IGP of India2,11.On an average, fertilizer was the first and second largest source of energy consumption in rice and wheat in all scenarios (Figs. 1 and 2), respectively. Aggregate proof from the current study and other similar studies highlighted that fertilizer consumption created the major share of the total energy input in crop production10,11,12. Among different fertilizers, N-fertilizers consumed the most energy input and constituted 94% in Sc1 and 87% in CSAP of the energy from fertilizers in RW system. From several past evidences, it is crystal clear that fertilizer application is exceeded to the highest demand for crop growth & development in this region, that further encouraged low resource use efficiency (RUE) and higher environmental footprints23,24. Thus, it is necessary to use fertilizers efficiently to reduce energy use and to prevent environmental degradation. Overall, the higher energy input was allied with more tillage, labour, irrigation and higher use of N-fertilizers in Sc1 compared to CSAP. Erenstein et al.6, Gathala et al.9and Ladha et al.3 also described that more tillage for seed bed preparation, more number of irrigation, higher labour and higher fertilizer inputs are the main interventions for higher energy usage under traditional farming. The higher output energy of rice, wheat and RW system with CSAP might be due to the multiple effects of applied nutrients1, zero tillage5, residue management, improved soil health2, good water regimes5,11and improved nutrient use efficiency (NUE) relative to Sc1. The CSAP recorded greater crop yields that ultimately reflected to greater net energy, EUE, human energy profitability, EP, over conventional methods of RW system.Energy use efficiency (EUE) and productivityEnergy use efficiency is an index used to measure the amount of energy that is effectively used in different farm activities. The highest input and the lowest output energy under Sc1 resulted into the lowest EUE and energy productivity (EP). Contrarily, the lowest energy input and the highest energy output under CSAP (mean of Sc4, Sc5 and Sc6) resulted into the maximum EUE and EP in both the crops in all the study’s years (Table 3). The average energy use efficiency was 52, 53 and 54% higher under Sc6 in rice, wheat and RW system compared to Sc1 (Table 3), respectively. CSAP recorded 44% (7.57 MJ MJ−1) higher EUE compared to Sc1 (5.28 MJ MJ−1) in the RW system. Linear contrast effects were also significant to EUE in rice, wheat and RW production systems. The large gap among the two values was due to tillage, irrigation and fertilizers which highlighted that EUE can be enhanced with reduced tillage, precision use of irrigation water and nutrient. Remarkably, the values observed in the current finding fall around the range described by other researchers11 who revealed that the EUE of RW production in IGP ranged 3.94 ± 1.31 MJ MJ−1. Overall, the results of the current study showed that those existing production methods of the RW system in IGP are not too efficient. Besides, RW system is damaging to agro-ecosystems because of imbalance and excess use of inputs. Hence, efficient use of production inputs would be helpful in optimizing energy consumption in RW system in the IGP region of South Asia.Energy productivity (EP) was statistically higher in the Sc6 in rice (0.15 kg MJ−1), wheat (0.21 kg MJ−1) and RW system (0.17 kg MJ−1) than in the Sc1 (Table 3). These findings revealed that an additional ~ 27% of RW system yield was gained per unit energy input in the Sc6 compared with the other scenarios (0.20 kg MJ−1). CSAP recorded 40% higher EP compared to Sc1 (0.17 kg MJ−1) in RW system. Linear contrast effects were significant to EP in rice, wheat and RW production systems (Table S2). The EP indices can be used for assessing the crop production associated environmental effects25. About agro-ecosystem sustainability, earlier research findings have highlighted that EP indicator could be used to judge optimal land and crop management intensities11,14. This study suggests there is an enormous potential for enhancing the energy productivity and efficiency of RW system in the IGP. CSA scenarios (Sc4, Sc5 and Sc6) improved EUE and EP in rice, wheat as well as RW system, was due to lower energy input and higher energy output relative to Sc1. The findings of our research are in line with those who has described that CA-based management practices can reduce energy input and increase output4,5,11,14.Yields, farm profitability and economic efficiency (Eco-efficiency)The rice yields were not much influenced by different crop management. However, in wheat, CSAP (mean of Sc4, Sc5 and Sc6) produced 11–16% and 10–13% higher grain and biomass yield, respectively compared to BAU. The grain and biomass yield of RW system was improved by 4–8 and 6–9% under CSAP, respectively relative to Sc1 (3-years’ mean) (Fig. 4).The CSAP improved the net income of rice, wheat and RW system by 15, 21 and 23% (3-years’ mean), respectively relative to Sc1 (US$ 824 and 1009 and 1833 ha−1, respectively) (Fig. 4). Linear contrast effects were significant to the net income in rice, wheat and RW production systems (Table S2). Higher net income was associated with CSAP due to less cultivation cost in various crop production activities such as tillage, crop establishment and irrigation9. Researcher observed that escaping field operations particularly tillage puddling and manual transplanting in rice and adoption of ZTDSR minimized tillage and establishment costs by 79–85%. CSAP improved crop yields while reducing production costs resulting in greater profitability of the RW system.Figure 4Effect of management practices portfolios on net return and eco-efficiency in rice, wheat and RW system (Mean of 3 years). Where; Sc1, business as usual-conventional tillage (CT) without residue; Sc2, CT with residue; Sc3, reduce tillage (RT) with residue + recommended dose of fertilizer (RDF); Sc4, RT/Zero tillage (ZT) with residue + RDF; Sc5, ZT with residue + RDF + GreenSeeker + Tensiometer; Sc6, Sc5 + Nutrient expert. Values with different lower case (a–e) letters are significantly different between each scenarios at p  More

Author notesThese authors contributed equally: AM Carpenter, BA Graham.Authors and AffiliationsUniversity of Lethbridge, Lethbridge, AB, CanadaA. M. Carpenter, B. A. Graham & T. M. BurgBiological Sciences Department, Auburn University, Auburn, AL, USAA. M. CarpenterDenver Museum of Nature and Science, Denver, CO, USAG. M. SpellmanAuthorsA. M. CarpenterB. A. GrahamG. M. SpellmanT. M. BurgCorresponding authorCorrespondence to
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