THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES

Abstract Development and application of photogrammetric mass‐estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression s...

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Bibliographic Details
Published in:Marine Mammal Science
Main Authors: Proffitt, Kelly M., Garrott, Robert A., Rotella, Jay J., Banfield, Jeff
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2006
Subjects:
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Weddell
Weddell Seals
Online Access:http://dx.doi.org/10.1111/j.1748-7692.2006.00091.x
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1748-7692.2006.00091.x
https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1748-7692.2006.00091.x
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Summary:Abstract Development and application of photogrammetric mass‐estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression statistics and decreases model explanatory power. Thus, it is important to understand and account for prediction variance when addressing ecological questions that require use of estimated mass values. In a simulation study based on data collected from Weddell seals, we developed regression models of pup weaning mass as a function of maternal postparturition mass where maternal mass was directly measured and second where maternal mass was photogrammetrically estimated. We demonstrate that when estimated mass was used, the regression coefficient was biased toward zero and the coefficient of determination was 30% less than the value obtained when using maternal postparturition mass obtained from direct measurement. After applying bias correction procedures, however, the regression coefficient and coefficient of determination were within 2% of their true values. To effectively use photogrammetrically estimated masses, prediction variance should be understood and accounted for in all analyses. The methods presented in this paper are effective and simple techniques to explore and account for prediction variance.

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