Journal
JOURNAL OF MAMMALOGY
Volume 99, Issue 5, Pages 1065-1071Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/jmammal/gyy103
Keywords
body condition index; body mass index; fat stores; quantitative magnetic resonance; size-corrected body mass
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- Canada Foundation for Innovation
- University of Massachusetts at Amherst
- Department of Defense, Strategic Environmental Research and Development Program [W912HQ-16-C-0015]
- Ontario Ministry of Natural Resources
- Pennsylvania Department of Environmental Protection
- United States Fish and Wildlife Service
- Texas Tech University
- Kansas State University
Ask authors/readers for more resources
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 (R-2 < 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available