Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats

Citation

Material Information

Title:
Common condition indices are no more effective than body mass for estimating fat stores in insectivorous bats
Series Title:
Journal of Mammology
Creator:
McGuire, Liam P.
Kelly, Lewis A.
Baloun, Dylan E.
Boyle, W Alice
Cheng, Tina L. et al
Publisher:
Oxford Academic
Publication Date:
Language:
English

Subjects

Subjects / Keywords:
Body Condition Index ( local )
Body Mass Index ( local )
Fat Stores ( local )
Quantitative Magnetic Resonance ( local )
Size-Corrected Body Mass ( local )
Genre:
serial ( sobekcm )

Notes

Abstract:
Researchers often use simple body condition indices (BCI) to estimate the relative size of fat stores in bats. Animals determined to be in better condition are assumed to be more successful and have higher fitness. The most common BCI used in bat research are the ratio index (body mass divided by forearm length) or residual index (residuals of body mass-forearm length regression) of size-corrected body mass. We used data from previous and ongoing studies where body composition (fat mass and wet lean mass) was measured by quantitative magnetic resonance to test basic assumptions of BCI, determine whether BCI is an effective proxy of fat mass, and whether other approaches could be more effective. Using data from 1,471 individual measurements on 5 species, we found no support for the underlying assumption that, within species, bats with longer forearms weigh more than bats with shorter forearms. Intraspecific relationships between body mass and forearm length were very weak (R2 < 0.08 in all but one case). BCI was an effective predictor of fat mass, driven entirely by the relationship between fat mass and body mass. With little variation in forearm length, calculation of BCI is essentially equivalent to dividing body mass by a constant. We evaluated alternative approaches including a scaled mass index, using tibia length, or predicting lean mass, but these alternatives were not more effective at predicting fat mass. The best predictor of fat mass in our data set was body mass. We recommend researchers stop using BCI unless it can be demonstrated the approach is effective in the context of their research.
Original Version:
Journal of Mammology, Vol. 99, no. 5 (2018-10-10).

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University of South Florida Library
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University of South Florida
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