Latitudinal Patterns in Rodent Metabolic Flexibility
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(2) Metabolic Flexibility in Rodents E173. Figure 1: Graphical model representing our working hypothesis. Note that for each latitude, lower critical temperature (Tlc) is equal to annual mean temperature, while thermoregulatory maximum metabolic rate (MMRCOLD) is reached at the minimum temperature of the coldest month. Specific data used to build the model and its main assumptions are provided in a zip file (fig. S1; table S1).. Bozinovic et al. 2011), and (3) there is a negative correlation between nonshivering thermogenesis (i.e., the ability to produce metabolic heat without muscular activity) and environmental temperature (Rodriguez-Serrano and Bozinovic 2009). However, to the best of our knowledge, no study has examined the relationship between metabolic scope (an instantaneous measure of metabolic flexibility) and abiotic factors. Accordingly, the aim of this study is to evaluate the correlation between absolute metabolic scope during cold exposure—that is, maximum metabolic rate during cold exposure (MMRCOLD) minus basal metabolic rate (BMR)—and geographic and climatic variables among rodent species. On the basis of the idea that animals are economically designed (Suarez 1996; Diamond 1998), we hypothesize the following relationships between metabolic rates and environmental temperatures: (1) BMR is adjusted by selective processes to the temperature most frequently experienced by organisms (i.e., a temperature that could be well represented by the annual mean temperature) in such a way that body temperature increase over this environmental temperature, due to metabolic heat production for maintenance, puts organisms near their body temperature set point; and (2) in contrast, MMRCOLD is tuned by selective processes in order to allow animals to cope with the short-term coldest extreme temperatures experienced during a few time bouts throughout. the year (i.e., a temperature that could be well represented by the the minimum temperature of the coldest month). Thus, given that the differences between annual mean temperature and the minimum temperature of the coldest month increase with latitude, we predict that metabolic scope should also increase with latitude (fig. 1). Methods Database Description and Conventional Analyses To test our hypothesis, we used metabolic data compiled by Rezende et al. (2004) for a set of 48 rodent species (Dryad data: http://dx.doi.org/10.5061/dryad.q0h473vr). Specifically, we used data on minimum metabolic rate (BMR or resting metabolic rate [RMR]; hereafter, both referred to as RMR) and thermoregulatory maximum metabolic rate (hereafter, MMRCOLD) together with geographic and climatic data (i.e., latitude, altitude, rainfall, and annual mean maximum and minimum temperatures) obtained from weather stations located near collection sites. Methodological details on how these variables were assessed are explained elsewhere (see Rezende et al. 2004). The only change made to this data set was a correction of the geographic data for Chinchilla brevicaudata, which were collected in El Morro Negro, Chile (25⬚00S; located. This content downloaded from 146.155.028.040 on May 25, 2016 13:41:26 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c)..
(3) E174 The American Naturalist 3,000 meters above sea level [masl]; Cortes et al. 2003), instead of La Serena, Chile (29⬚54S; 169 masl), as stated by Rezende et al. (2004). Accordingly, and following the criteria proposed by Rezende et al. (2004), the climatic data for this species were obtained from the weather station at El Potrerillo (26⬚30S, 69⬚27W; 2,800 masl). In addition, for each collection site, we obtained data on annual mean temperature (Tmed), the minimum temperature of the * coldest month (Tmin ), and four measurements of climatic variability: temperature annual range (TAR; the difference between the maximum temperature of the warmest month and the minimum temperature of the coldest month), temperature monthly range (TMR; the average differences between the maximum and minimum monthly temperatures), temperature seasonality (TS; the standard deviation of the mean monthly temperature), and precipitation seasonality (PS; the standard deviation of the mean monthly rainfall). These climatic data were downloaded from the WorldClim database (http://www.worldclim.org). Also, for each species, we obtained data on natural hypothermic responses from the ISI Web of Science (using the search criteria “species name” plus “torpor” and “species name” plus “hibernation”) and from recent reviews on this topic (e.g., Lovegrove 2000; Geiser 2004). These data were then included, as a categorical covariate, in regression analyses (see below). Because all of the studies compiled by Rezende et al. (2004) use the same factor and methodology to induce MMRCOLD (i.e., cold exposure in a He-O2 atmosphere; see Rosenmann and Morrison 1974), we used absolute metabolic scope (MMRCOLD ⫺ RMR) as a measure of metabolic flexibility. Although this instantaneous measurement did not take into account long-term acclimation in metabolic rate (e.g., seasonal acclimation), it represented the ability to increase heat production over the basal level at a particular moment of the year, and thus it could be considered a good indicator of the “aerobic reserve” of each species (see Ochocinska and Taylor 2005). Moreover, data selection criteria used to build the original data set (i.e., when more than one value was available, values obtained in seasons other than winter were selected) are intended to minimize the recognized effects of seasonality on metabolic rates (see Rezende et al. 2004). In order to combine the effects of latitude and altitude into one variable, we estimated the corrected latitude (Cor Lat), using the following formula: Cor Lat p (altitude / 76.25) ⫹ latitude (Price et al. 1998). Because neither Rezende et al. (2004) nor many of the original sources reported error estimations for metabolic data, we did not attempt to use modern meta-analytic techniques (e.g., Hedges’ difference) to test our hypothesis. Thus, the relationships between metabolic scope and abiotic variables were evaluated through standard least squares regression techniques, using. body mass as a continuous covariate and hypothermic response as a categorical covariate. Given that only one species in our data set is considered a deep hibernator (Spermophilus beldingi), we pooled hibernation and daily torpor into a single group for these analyses. Given that Rezende et al. (2004) provide more than one data point for some species, conventional analyses were performed for two data sets: all cases (n p 57) and species data (using the mean values for each species; n p 48). However, because both data sets gave very similar results, herein we present only those results obtained for the allcases data set (results of conventional analyses for species’ mean values are given in table S2, available in a zip file). Finally, in order to test for the effect of some potential confounding factors that could affect the correlation between metabolic scope and corrected latitude (i.e., the best predictor of metabolic flexibility; see “Results”), we also analyzed the association between corrected latitude and (1) the type of minimum metabolic rate used (BMR or RMR) and (2) species’ trophic categories (herbivorous, granivorous, or omnivorous; also taken from Rezende et al. 2004). The effects of these factors were evaluated through one-way ANCOVAs (with body mass and hypothermic response as covariates), using the statistical package Statistica, version 8.0 (Statsoft). Phylogenetically Informed Analyses To evaluate the effect of phylogeny on the relationship between metabolic scope and abiotic variables, we used a Bayesian phylogenetic mixed model (Bayesian PMM; Naya et al. 2006; Hadfield 2010) plus Bayesian model averaging (BMA; Raftery et al. 1997). By taking the phylogenetic tree published by Rezende et al. (2004) as the starting point, we decided to use BMA because (1) branch lengths were not known for this tree and (2) there are some soft polytomies associated with this uncertainty. In this sense, phylogenetic uncertainty was included by generating 1,000 trees in which polytomies were randomly resolved (by transforming all multichotomies into a series of dichotomies with one or several branches of length 0) and branch lengths were randomly sampled from a uniform distribution (ranging between 0.01 and the maximum branch length). For each comparative model, the effect of each abiotic variable on metabolic scope was calculated through linear mixed models, using body mass as a continuous covariate and hypothermic response as a categorical covariate. Then, to estimate the effect of each independent variable on metabolic scope, we calculated the proportion of posterior estimates greater than 0 (gt0). In short, gt0 can be viewed as the probability of observing a positive (if gt0 1 0.5) or negative (if gt0 ! 0.5) association between the dependent variable (e.g., metabolic scope) and each. This content downloaded from 146.155.028.040 on May 25, 2016 13:41:26 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c)..
(4) Metabolic Flexibility in Rodents E175 abiotic variable (e.g., latitude). Note that when the dependent variable is not affected by the independent variable, this probability would be equal to 0.5 (i.e., the distribution of the regression coefficients would be centered on 0). In addition, for each case, the amount of information provided by the phylogeny was evaluated using the phylogenetical signal values (Ps), which estimate the proportion of phenotypic variation that is phylogenetically inherited between species (this parameter was called H 2 in Lynch 1991). The Ps value ranges between 0 (i.e., no phylogenetic effect on the inheritance of a character) and 1 (i.e., total dependence between the character state of the ancestor and the character state of the descendent). All comparative analyses were performed using the freely available software R, through the APE (Paradis et al. 2004) and bmaMCMCanalysis (L. Spangenberg, R. Romero, and H. Naya, unpublished software; available upon request) packages. As in conventional analyses, the association between metabolic scope and abiotic variables were analyzed for two data sets (i.e., including all cases and species’ mean values), but given the similarity between them we present only those results for the all-cases data set (results of phylogenetically informed analyses for species’ mean values are provided in table S2, available in a zip file). Results Conventional analyses showed a positive correlation between metabolic scope and corrected latitude and a negative correlation between metabolic scope and both annual mean minimum and annual mean temperatures (table 1; fig. 2). In addition, metabolic scope appears to be positively correlated with altitude and negatively correlated with the minimum temperature of the coldest month, but in these cases the correlation reaches only a marginal probability value. Phylogenetically informed analyses provided results that were very similar to those obtained by conventional methods, supporting the fact that phylogenetic signal values (Ps) were markedly low in all of the cases (table 1). Again, a positive correlation was found between metabolic scope and corrected latitude and a negative correlation was observed between metabolic scope and both annual mean minimum and annual mean temperatures (table 1). In addition, the previously mentioned positive relationships observed between metabolic scope and altitude and between the former variable and the minimum temperature of the coldest month were both significant in these analyses (table 1). Regarding the proximal causes of the latitudinal pattern described above, we found that in agreement with our hypothesis, RMR tended to be negatively correlated with annual mean temperature and MMR was negatively cor-. Table 1: Partial correlation coefficients and their associated probabilities between metabolic scope and geographic and climatic variables Conventional analyses. Latitude Altitude Cor Lat Rainfall Tmax Tmin * Tmin Tmed TAR TMR TS PS. Phylogenetically informed analyses. r. P. B (Ⳳ1 SD). gt0. Ps (upper limit). .03 .10 .13 1⫺.01 ⫺.06 ⫺.13 ⫺.11 ⫺.12 .05 .07 .03 ⫺.01. .58 .08 .02 .96 .28 .02 .06 .04 .39 .21 .63 .87. 1.11 (1.99) .03 (.02) 3.43 (1.46) ⫺.002 (.05) ⫺3.75 (3.42) ⫺8.00 (3.46) ⫺4.81 (2.53) ⫺7.40 (3.56) 2.21 (2.55) 9.00 (7.17) .04 (.07) ⫺.07 (.45). .72 .96 .99 .48 .13 .01 .03 .02 .81 .90 .69 .44. .00001 .0000003 .00001 .00001 .00001 .00001 .00001 .00001 .00001 .0000003 .00001 .00001. Note: B indicates the mean slope value estimated from the linear model; for other abbreviations, see “Methods.” The covariate body mass was highly significant in each case (r 1 0.90 , P ! .0001 ), while the covariate hypothermic response was never significant (r ! 0.03, P 1 .70).. related with minimum temperature of the coldest month (table 2). In addition, the differences between these two temperatures increased with corrected latitude (table 2). However, the difference between annual mean temperature and the minimum temperature of the coldest month was better correlated with uncorrected latitude than with corrected latitude (table 2), although the former variable was not correlated with metabolic scope. Moreover, the difference between both environmental temperatures was not correlated with altitude, even though altitude has a marginal effect on metabolic scope (table 2). Interestingly, when species living above 2,000 masl are removed from the analyses, it is observed that (1) the difference between annual mean temperature and minimum temperature of the coldest month is strongly correlated with uncorrected latitude, corrected latitude, and altitude (table 2); (2) uncorrected latitude (r p 0.10, P ! .05), corrected latitude (r p 0.13, P ! .01), and altitude (r p 0.11, P ! .03) became significant predictors of metabolic scope (fig. S2, available in a zip file). Finally, regarding the effect of potential confounding factors, it should be noted that although studies using BMR were more common at higher corrected latitudes (F1, 55 p 6.03, P p .02), there was no correlation between the type of minimum metabolic rate measured and metabolic scope (F1, 53 p 3.11, P p .08). In this sense, when studies using BMR were removed from the analyses, the correlations between metabolic scope and corrected latitude did not differ from those observed when both BMR. This content downloaded from 146.155.028.040 on May 25, 2016 13:41:26 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c)..
(5) 0,8. Residuals of metabolic scope. (A) r = 0.54, F1,55 = 22.1, P < 0.00002 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6 -0,8. 0. 10. 20. 30. 40. 50. 60. 70. 80. Corrected latitude (º) 0,8. Residuals of metabolic scope. (B) r = -0.48; F1,55 = 16.5, P < 0.0002 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6 -0,8 -10. -5. 0. 5. 10. 15. 20. 25. 30. Annual mean minimum temperature (ºC) 0,8. Residuals of metabolic scope. (C) r = -0.49, F1,55 = 17.4, P < 0.0002 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6 -0,8 -10. -5. 0. 5. 10. 15. 20. 25. 30. Annual mean temperature (ºC). E176 This content downloaded from 146.155.028.040 on May 25, 2016 13:41:26 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c)..
(6) Metabolic Flexibility in Rodents E177 and RMR were included in the analyses (r p 0.10, P p .02). With regard to dietary habits, there was no association between species trophic categories and metabolic flexibility (F2, 52 p 1.18, P p .31) or between species trophic categories and corrected latitude (F2, 52 p 1.01, P p .37). Discussion Our planet is undergoing dramatic environmental changes, referred to as global change (Gaston and Spicer 2004; Matesanz et al. 2010). Within this context, several studies published recently have suggested that the ability of species to cope with accelerated changing conditions will be closely related to the current amount of plasticity for fitness-related traits (Berteaux et al. 2004; Charmentier et al. 2008; Deutsch et al. 2008; Gienapp et al. 2008; Hendry et al. 2008; Hoffman and Sgro 2011). From these results, it is clear that identifying global geographic patterns in phenotypic flexibility should be considered a major goal in the current ecological research agenda. As we mentioned above, direct evidence supporting the CVH came mainly from the analysis of tolerance ranges of ectothermic species. However, in recent years, direct support for the CVH has been expanded to endothermic animals (Naya et al. 2008). In this respect, we found here that metabolic scope, a measure of metabolic flexibility, is correlated with corrected latitude, a result that was observed in both conventional and phylogenetically informed analyses. Moreover, when high-altitude points were removed from the analyses, the correlation between metabolic scope and either uncorrected latitude or altitude also became significant, as predicted by the CVH. In line with this, when climatic data for these high-altitude points are analyzed, it is observed that the mean and minimum temperatures are noticeably higher than expected for its latitude (on the basis of the points !2,000 masl), while thermal amplitude is markedly lower (fig. S3, available in a zip file). Although we do not know to what extent this reduction in thermal amplitude is a simple anomaly of our data set or whether it is a more general climatic phenomenon, its mere existence allowed for a further test that confirms our hypothesis. On the other hand, we found that latitude is a better predictor of physiological flexibility than the proximal (putative) climatic variables (table 1; fig. 2). This finding has been observed in other studies, and there are at least two explanations for it. First, latitude is probably a better pre-. Figure 2: Relationship between residuals of metabolic scope with regard to body mass (both in natural logarithm) and A, corrected latitude, B, annual mean minimum temperature, and C, annual mean temperature.. Table 2: Correlation coefficients between metabolic rates (resting [RMR] and maximum [MMR]) and environmental * temperatures (Tmed and Tmin ) and between thermal amplitude * (Tmed ⫺ Tmin) and geographic variables r All points included: RMR vs. Tmed * MMR vs. Tmin * Tmed ⫺ Tmin vs. Cor Lat * Tmed ⫺ Tmin vs. latitude * Tmed ⫺ Tmin vs. altitude Only points below 2,000 masl: RMR vs. Tmed * MMR vs. Tmin * Tmed ⫺ Tmin vs. latitude * Tmed ⫺ Tmin vs. Cor Lat * Tmed ⫺ Tmin vs. altitude. P. ⫺.06 ⫺.10 .38 .55 ⫺.04. .06 .04 .004 .00001 .74. ⫺.09 ⫺.10 .56 .65 .39. .001 .016 .00003 .000001 .006. Note: For definitions of geographic and climatic variable abbreviations, see “Methods.”. dictor of long-term regimens of climatic variables than current climate values provided by weather stations. Second, latitude is correlated with several other, not assessed climatic (e.g., wind speed), ecological (e.g., day length, environmental productivity), and historical factors (e.g., biogeographical boundaries) that could affect physiological traits (Speakman 2000; Rezende et al. 2004; Naya et al. 2008). Nevertheless, while we share the logic of these arguments, we believe that there is a simpler and probably more relevant unseen cause behind this result, namely, a spatial scale problem. This problem can be enunciated as follows: (1) each species included in our analyses is present not only at its collection site but throughout its entire distributional range, that is, in an area that is usually much larger than the collection site; (2) if genetic flux occurs within a large portion of the species’ distributional range, adaptation to environmental conditions should not be more fine-tuned than adaptation to the conditions occurring over this large area; and (3) given the smooth variation of climatic variables in the space, latitude should represent a weighted variable of climatic conditions acting over spatial scales that are much closer to the species’ distributional range—and thus, much closer to the scale at which adaptation is expected to occur—than the climatic data at the collection sites. This may be the reason why geographical latitude is usually a better predictor of physiological traits than local climatic conditions, despite the fact that climatic variables (and not latitude) are usually the ultimate factors underlying physiological variation (Maldonado et al. 2011; Naya et al. 2011). In conclusion, although global patterns of phenotypic flexibility—which are different from thermal tolerance ranges in ectothermic species—are starting to emerge. This content downloaded from 146.155.028.040 on May 25, 2016 13:41:26 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c)..
(7) E178 The American Naturalist (Naya et al. 2008; Smit and McKechnie 2010; this study), we are far from having a clear understanding of the causal mechanisms behind them. The conceptual model stated in this study represents an effort in this direction.. Acknowledgments E. Rezende kindly provided data to perform phylogenetic analyses, and C. Abud and P. E. Neill made useful suggestions to the manuscript. This study was supported by funding by Comisión Sectorial de Investigación Cientı́fica (Uruguay; to D.E.N.), Agencia Nacional de Investigación e Innovación (Uruguay; to L.S.), and Fondo de Financimiento de Centros de Excelencia en Investigación (FONDAP) 15010001 Program 1 (Chile) and Programa Iberoamericano De Ciencia y Tecnologı́a para el Desarrollo (CYTED) network ECONS (410RT0406; to D.E.N. and F.B.).. Literature Cited Addo-Bediako, A., S. L. Chown, and K. J. Gaston. 2000. Thermal tolerance, climatic variability and latitude. 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![Table 2: Correlation coefficients between metabolic rates (resting [RMR] and maximum [MMR]) and environmental temperatures (T med and T min* ) and between thermal amplitude (T med ⫺ T min* ) and geographic variables](https://thumb-us.123doks.com/thumbv2/123dok_es/7283443.443533/6.918.479.805.209.437/correlation-coefficients-metabolic-environmental-temperatures-amplitude-geographic-variables.webp)
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Based on these data and considering that metabolic proteins and those soluble in chloroform/methanol, such as α-amylase, β-amylase and serpins, are related to food and
Objective: We aimed to investigate the effect of ethnicity, together with sex and pubertal status, on body fat content and distribution, serum adipokine profile, metabolic
2,14E-17 aromatic compound biosynthetic process 2,14E-17 cellular aromatic compound metabolic process 1,69E-16 cellular amino acid and derivative metabolic process 1,69E-16
ABSTRACT: Herein we report in vitro metabolic stability in human liver microsomes (HLMs), interactions with cytochrome P450 isoenzymes (CYP3A4, CYP2D6, and CYP2C9), and
The increment in the glucose metabolic index at the lower insulin infusion rate over that observed under basal condi- tions can be compared with the maximal increment obtained
