Hematological data of Batrachuperus tibetanus
Cite this dataset
Xiong, Jianli et al. (2021). Hematological data of Batrachuperus tibetanus [Dataset]. Dryad. https://doi.org/10.5061/dryad.rv15dv46t
Intraspecific variation is a common phenomenon in nature, but to date, research on such variation in hematological traits of urodeles remains scarce. To compare intraspecific variation in hematological traits among populations, and explore whether snout-vent length (SVL) and body mass influence hematological traits, we analyzed hematological parameters and erythrocyte size in 58 mature Batrachuperus tibetanus specimens belonging to three populations (Xihe, Meixian and Taibai) in northwestern China. There were no sexual differences in any hematological trait for all populations. No hematological traits differed significantly between the Meixian and Taibai populations, but significantly lower values of erythrocyte count (RBC), hemoglobin (Hb), hematocrit (Hct), erythrocyte length (L), and erythrocyte area (A), as well as a significantly higher leukocyte count (WBC) were observed in the Xihe population compared with the other two. Linear regression analyses showed that significant relationships were present between SVL and Hb, RBC, Hct, and L; and body mass and Hb, RBC, Hct, and L. However, SVL and WBC were negatively correlated. Only L differed significantly among populations when accounting for the effects of SVL and body mass. These results indicate that among-population variation in Hb, RBC, WBC, and Hct possibly contributes to differences in SVL or body mass, and variation in erythrocyte size (L and A) is perhaps attributable to differences in lower Hb, RBC, and Hct. Our results provide a foundation for understanding the physiological basis of adaptation in urodeles.
Live animals were first anesthetized with MS-222 (Yufobao, Fujian Jinjiang Shengyuan Aquatic Products Company). Prior to blood collection, the snout-vent length (SVL) of each individual was measured to the nearest 0.1 mm using digital calipers. Body mass was measured using an electronic balance with an accuracy of 0.1 g. Sex was determined via inspection of the gonads. Finally, blood samples were obtained directly from the aortic arch of each salamander by heparinized hematocrit capillaries.
Erythrocyte count (RBC) was determined with a Neubauer hemocytometer under an OLYMPUS CX31 light microscope (Olympus, Tokyo, Japan). The leukocyte count (WBC) was calculated via the proportion of erythrocytes to leukocytes in 10 randomly chosen fields with the aid of a grid ocular micrometer. Hemoglobin (Hb) concentration was measured with Sahli haemoglobinometer. Hematocrit (Hct) was determined by standard centrifugation in microhematocrit tubes. Mean cell volume (MCV), mean cell hemoglobin (MCH), and mean cell hemoglobin concentration (MCHC) were calculated according to Wintrobe’s (1933) formula. Three blood smears per individual were prepared using the push slide technique to examine erythrocyte morphometry. Blood smears were stained with Wright’s stain and examined under an OLYMPUS CX31 light microscope. For the morphometric analysis, 50 erythrocytes were randomly extracted per individual, and erythrocyte length (L), width (W), nucleus length (NL), and nucleus width (NW) were measured using the Biolife Std microscopic image analysis software (Beijing iCALIBUR Research & Development Center). Erythrocyte area (A) and nucleus area (NA) were calculated according to the equations A = ELEWπ/4 and NA = NLNWπ/4, respectively (Tosunoğlu et al., 2011).
Prior to analysis, all variables were tested for normality using Kolmogorov-Smirnov tests and homogeneity of variances using Levene's test. Non-homogeneous variables were log10-transformed. A two-way analysis of variance (ANOVA) was used to test for effects of sex. If no sexual dimorphism was present, data for both sexes were pooled for each population, and then the effect of population was assessed by two-way ANOVA. Linear regression was used to determine the relationships between significantly different hematological traits with body size and body mass. The effects of SVL and body mass on significantly correlated traits among populations were assessed by an analysis of covariance (ANCOVA) using SVL and body mass as the respective covariates, with (SVL * population) and (body mass * population) as additional independent variables to test for differences in slope. If there was no difference in slope, the interaction term was dropped from the model and the analysis re-run. All statistical tests were performed with SPSS software, Version 22.0 (IBM, Armonk, NY, USA), with all statistical tests set to be two-tailed at a significance level of α = 0.05.
National Natural Science Foundation of China, Award: 31471971
Ecological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Award: ESP-2006