Estimating lipid and lean body mass in small passerine birds using TOBEC, external morphology and subcutaneous fat-scoring

To assess regression models for lipid and lean body mass in small birds, we recorded live body mass +/- 0.1 g, total body electrical conductivity (TOBEC; from third generation TOBEC machine EM-SCAN (R) SA-3000) or E-Value, visual fat score (VisFat), and seven body measurements for 52 migratory passerine birds of 13 species (5-40 g). We determined lipid and lean mass of each bird after petroleum-ether extraction of lipids. We obtained net E-Value (NEV) for each scanned bird by subtracting the E-Value of the empty bird-restraining tube, because these showed an inverse temperature dependence (P < 0.005). Leave-one-out cross validation was used to assess model selection and construct 95% confidence intervals. Although precision of TOBEC increased with bird size (CV of NEV vs. live mass: r=-0.276, P=0.002) and it explained an increasing proportion of variation in lean mass moving from small- to medium- to large-bird classes of our data, it did no better than head length in single-variable prediction of lean or lipid mass and was included in five of the 14 multivariate models we developed. The best multiple regression to predict lean mass included live weight, VisFat, bill length, tarsus and lnNEV (adjusted R-2=99.0%); however, the same model lacking only lnNEV yielded aR(2)=98.9%. A parallel to the above pair of models, but predicting lipid mass, yielded aR(2)=90.3% and 90.0%, respectively. Subdividing the data by three size classes and three taxa (American redstart Setophaga ruticilla, ovenbird Seiurus aurocapilla, warblers), best-subset multiple-regression models predicted lean mass with aR(2) from 94.7 to 99.6% and lipid mass with aR(2) from 85.4 to 98.3%. Best models for the size- and species-groups included VisFat and zero to five body measurements, and most included live weight. lnNEV was included only in the models for ovenbird (lipid), warblers (lipid), all birds (both), and large birds (both). Actual lipid mass of all birds was more highly correlated with multiple-regression-predicted lipid mass (r=0.955) than with visual subcutaneous fat-scoring (r=0.683). These multiple-regression models predicting lipid content using live-bird measurements and visual fat score as independent variables represent more accurate and precise estimates of actual lipid content in small passerines than any previously published. They are particularly accurate for placing birds into percentage body-fat classes.