DOI: 10.5061/dryad.rv15dv46t
title: Hematological data of Batrachuperus tibetanus

description: 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.
description: 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.