| Literature DB >> 30051854 |
Julia V Tejada-Lara1,2,3, Bruce J MacFadden4, Lizette Bermudez5, Gianmarco Rojas5, Rodolfo Salas-Gismondi2,3,6, John J Flynn2.
Abstract
Carbon isotopic signatures recorded in vertebrate tissues derive from ingested food and thus reflect ecologies and ecosystems. For almost two decades, most carbon isotope-based ecological interpretations of extant and extinct herbivorous mammals have used a single diet-bioapatite enrichment value (14‰). Assuming this single value applies to all herbivorous mammals, from tiny monkeys to giant elephants, it overlooks potential effects of distinct physiological and metabolic processes on carbon fractionation. By analysing a never before assessed herbivorous group spanning a broad range of body masses-sloths-we discovered considerable variation in diet-bioapatite δ13C enrichment among mammals. Statistical tests (ordinary least squares, quantile, robust regressions, Akaike information criterion model tests) document independence from phylogeny, and a previously unrecognized strong and significant correlation of δ13C enrichment with body mass for all mammalian herbivores. A single-factor body mass model outperforms all other single-factor or more complex combinatorial models evaluated, including for physiological variables (metabolic rate and body temperature proxies), and indicates that body mass alone predicts δ13C enrichment. These analyses, spanning more than 5 orders of magnitude of body sizes, yield a size-dependent prediction of isotopic enrichment across Mammalia and for distinct digestive physiologies, permitting reconstruction of foregut versus hindgut fermentation for fossils and refined mean annual palaeoprecipitation estimates based on δ13C of mammalian bioapatite.Entities:
Keywords: body mass; digestive physiology; isotope fractionation; mammals; sloths; stable isotopes
Mesh:
Substances:
Year: 2018 PMID: 30051854 PMCID: PMC6030519 DOI: 10.1098/rspb.2018.1020
Source DB: PubMed Journal: Proc Biol Sci ISSN: 0962-8452 Impact factor: 5.349
Summary of isotopic analyses: δ13C and ε* (diet, dental bioapatite and faeces) for sloths with known diets (through controlled-feeding (Bradypus, Choloepus) or constrained-diet faeces analyses (aMylodon)).
| (a) summary of isotopic data of sloths’ diets | ||||||
|---|---|---|---|---|---|---|
| taxa | diet component | δ13Cdiet component (‰) | contribution of each element [δ13C (‰)] to final signature of dietb | δ13Cdiet (‰) | ||
| rubber plant ( | −28.67 ± 1.79 | Monospecific diet | −28.67 ± 1.79 | |||
| Purina® DogChow® | −16.5 | −1.50 | −26.37b | |||
| quinoa | −25.8 | −2.28 | ||||
| broccoli | −27.3 | −3.75 | ||||
| sweet potato | −27.9 | −4.66 | ||||
| carrot | −25.3 | −4.14 | ||||
| spinach | −28.1 | −7.29 | ||||
| rubber plant | −29.4 | −2.75 | ||||
| a | calculated from dung | — | −27.17c to −28.14d | |||
arecently extinct Pleistocene sloth.
bcalculation based on a concentration-weighted linear mixing model [20].
cassuming diet-faeces enrichment similar to that of Bradypus.
dassuming diet-faeces enrichment similar to that of Choloepus.
Figure 1.Mammalian phylogenetic tree for sampled herbivores, mapped in a space defined by δ13C diet-bioapatite enrichment . Colours represent clades (see the electronic supplementary material). Note the extremely large range of variation in Folivora (sloths), spanning almost the entire spectrum of variation observed in Mammalia. Animal silhouettes are for reference and not to scale.
Figure 2.Correlations between body mass and in controlled and constrained diet studies. The regression formula across all mammalian herbivores (dotted line) should be applied when type of fermentation of the herbivorous mammal under study is unknown, or does not fall definitively within foregut or hindgut types of herbivore fermentation (e.g. giant panda). Formulae for foregut (greenish line) and hindgut (red line) fermenters should be applied when the type of digestive fermentation (foregut or hindgut) of the mammal under study is known or probable given its phylogenetic history (see the electronic supplementary material). Species included are: (1) Mus musculus, house mouse; (2) Microtus ochrogaster, prairie vole; (3) Oryctolagus cuniculus, European rabbit; (4) Choloepus hoffmanni, two-toed sloth; (5) Bradypus variegatus, three-toed sloth; (6) Phascolarctos cinereus, koala; (7) Sus scrofa, pig; (8) Ailuropoda melanoleuca, giant panda; (9) Lama guanicoe, guanaco; (10) Equus burchelli, zebra; (11) Equus caballus, horse; (12) Camelus bactrianus, Bactrian camel; (13) Bos taurus, cow; (14) Diceros bicornis, black rhinoceros; (15) Giraffa camelopardalis, giraffe; (16) Loxodonta africana, African elephant; (17) Mylodon darwinii, giant ground sloth (extinct). All values are in natural logarithmic scale. BM in kg.
Figure 3.Correlations between and different life-history traits. (a) Basal metabolic rate (BMR). (b) BMR, after adjusting for the body mass component of the measured BMR (BMR/Kleiber value). The body mass component was removed by adjusting the BMR to a value corresponding to 3.42*BM−0.25 (i.e. the Kleiber value [22]), indicating the scaling of the BMR relative to the body mass for mammals (body mass in grams [23]). (c) Average rectal temperature and (d) breadth of range of rectal temperature. All values in natural logarithmic scale. Temperature in °C. Dotted black line is the regression model for all taxa included; the red and greenish lines are the regression models for hindgut and foregut fermenters, respectively. Numbers for taxa same as in figure 2.
Summary of quantitative analyses. ((a) Summary of coefficients and significance values for linear regressions performed. Quantile regression estimates the conditional median of the response variable, unlike ordinary least squares (OLS) that estimates the approximate conditional mean. Robust regression fittings (objective functions used were Huber and Tukey bisquare) are performed by iterated, re-weighted least-squares analyses. The formulae proposed use the average of the coefficients given by the different regression models (see the electronic supplementary material). (b) Component models (delta AICc < 2) that best predict δ13C diet–bioapatite enrichment in herbivorous mammals given current data. Body mass alone is the best predictor of enrichment. Other, more complex models that simultaneously assess more potential influences, do not significantly improve prediction of the enrichment in the AICc analyses. Abbreviations: d.f., degrees of freedom; logLik, maximum log-likelihood; AICc, Akaike information criterion corrected for small sample sizes; delta, difference in AICc between the current and the best model; weight, prior weights used in model fitting.)
| ( | intercept | slope | |||
|---|---|---|---|---|---|
| OLS model fit | 2.4 | 0.034 | 0.62 | 0.0002 | |
| OLS model fit excluding outliers | 2.39 | 0.04 | 0.83 | 5.35×10−6 | |
| quantile regression fit | 2.37 | 0.041 | |||
| robust regression (Huber estimator) | 2.4 | 0.036 | |||
| robust regression (Tukey bisquare estimator) | 2.4 | 0.035 | |||
| average | 2.39 | 0.037 | |||
| OLS model fit | 2.42 | 0.032 | 0.74 | 0.003 | |
| OLS model fit excluding outliers | 2.4 | 0.036 | 0.89 | 0.001 | |
| quantile regression fit | 2.48 | 0.022 | |||
| robust regression (Huber estimator) | 2.43 | 0.03 | |||
| robust regression (Tukey bisquare estimator) | 2.51 | 0.019 | |||
| average | 2.45 | 0.028 | |||
| OLS and robust model values | 2.34 | 0.05 | 0.78 | 0.008 | |
| quantile regression fit | 2.24 | 0.07 | |||
| robust regression (Huber estimator) | 2.34 | 0.05 | |||
| robust regression (Tukey bisquare estimator) | 2.34 | 0.05 | |||
| average | 2.34 | 0.06 |