ABSTRACT. A major difficulty in the application of probabilistic models to estimations of mammal abundance is obtaining a data set that meets all of the assumptions of the model. In this paper, we evaluated the concordance correlation among three population size estimators, the minimum number alive (MNA), jackknife and the model suggested by the selection algorithm in CAPTURE (the best-fit model), using long-term data on three Brazilian small mammal species obtained from three different studies. The concordance correlation coefficients between the abundance estimates indicated that the probabilistic and enumeration estimators were highly correlated, giving concordant population estimates, except for one species in one of the studies. The results indicate the adequacy of using enumeration estimates as indexes for population size when scarce data do not allow for the use of probabilistic methods. Differences observed in the behavior of enumeration and probabilistic methods among species and studies can be related to the exclusive sampling design of each area, species-specific movement characteristics and whether a significant portion of the population could be sampled.

It is expensive and laborious to evaluate carcass composition in beef cattle. The objective of this study was to evaluate a method to predict the 9th to 11th rib section (rib) composition through empirical equations using dual energy X-ray absorptiometry (DXA). Dual energy X-ray absorptiometry is a validated method used to describe tissue composition in humans and other animals, but few studies have evaluated this technique in beef cattle, and especially in the Zebu genotype. A total of 116 rib were used to evaluate published prediction equations for rib composition and to develop new regression models using a cross-validation procedure. For the proposed models, 93 ribs were randomly selected to calculate the new regression equations, and 23 different ribs were randomly selected to validate the regressions. The rib from left carcasses were taken from Nellore and Nellore × Angus bulls from 3 different studies and scanned using DXA equipment (GE Healthcare, Madison, WI) in the Health Division at Universidade Federal de Viçosa (Viçosa, Brazil). The outputs of the DXA report were DXA lean (g), DXA fat free mass (g), DXA fat mass (g), and DXA bone mineral content (BMC; g). After being scanned, the rib were dissected, ground, and chemically analyzed for total ether extract (EE), CP, water, and ash content. The predictions of rib fat and protein from previous published equations were different ( < 0.01) from the observed composition. New equations were established through leave-one-out cross-validation using the REG procedure in SAS. The equations were as follows: lean (g) = 37.082 + 0.907× DXA lean ( = 0.95); fat free mass (g) = 103.224 + 0.869 × DXA fat free mass ( = 0.93); EE mass (g) = 122.404 + 1.119 × DXA fat mass ( = 0.86); and ash mass (g) = 18.722 + 1.016 × DXA BMC ( = 0.39). The equations were validated using Mayer's test, the concordance correlation coefficient, and the mean square error of prediction for decomposition. For both equations, Mayer's test indicated that if the intercept and the slope were equal to 0 and 1 ( > 0.05), respectively, then the equation correctly estimated the rib composition. Comparing observed and predicted values using the new equations, Mayer's test was not significant for lean mass ( = 0.26), fat free mass ( = 0.67), EE mass ( = 0.054), and ash mass ( = 0.14). We concluded that the rib composition of Nellore and Nellore × Angus bulls can be estimated from DXA using the proposed equations.