To distinguish species or populations using morphometric data is generally processed through multivariate analyses, in particular the discriminant analysis. We explored another approach based on the maximum likelihood method. Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data (or sample) in a given reference group. When data are described by more than one variable, the maximum likelihood (MLi) approach allows to combine these chances to find the best fit for the data. Such approach assumes independence between variables. The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics, but improvements may be obtained after some mathematical transformations. Provided there is strict anatomical correspondence of variables between unknown and reference data, the MLi classification produces consistent classification. We explored this approach using various input data, and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification. The simplicity of the method, its fast computation, performance and versatility, make it an interesting complement to other classification techniques.

To distinguish species or populations using morphometric data is generally processed through multivariate analyses, in particular the discriminant analysis. We explored another approach based on the maximum likelihood method. Simple statistics based on the assumption of normal distribution at a single variable allows to compute the chance of observing a particular data (or sample) in a given reference group. When data are described by more than one variable, the maximum likelihood (MLi) approach allows to combine these chances to find the best fit for the data. Such approach assumes independence between variables. The assumptions of normal distribution of variables and independence between them are frequently not met in morphometrics, but improvements may be obtained after some mathematical transformations. Provided there is strict anatomical correspondence of variables between unknown and reference data, the MLi classification produces consistent classification. We explored this approach using various input data, and compared validated classification scores with the ones obtained after the Mahalanobis distance-based classification. The simplicity of the method, its fast computation, performance and versatility, make it an interesting complement to other classification techniques.