Comparison of discriminant function and classification tree analyses for age classification of marmots

Abstract

2004. Comparision of discriminant function and classification tree analyses for age classification of marmots. Á/ Oikos 105: 575 Á/587.We evaluated the predictive power of two classification techniques, one parametric Á/ discriminant function analysis (DFA) and the other non-parametric Á/ classification and regression tree analysis (CART), in order to provide a non-subjective quantitative method of determining age class in Vancouver Island marmots (Marmota vancouverensis ) and hoary marmots (Marmota caligata ). For both techniques we used morphological measurements of known-age male and female marmots from two independent population studies to build and test predictive models of age class. Both techniques had high predictive power (69 Á/86%) for both sexes and both species. Overall, the two methods performed identically with 81% correct classification. DFA was marginally better at discriminating among older more challenging age classes compared to CART. However, in our test samples, cases with missing values in any of the discriminant variables were deleted and hence unclassified by DFA, whereas CART used values from closely correlated variables to substitute for the missing values. Therefore, overall, CART performed better (CART 81% vs DFA 76%) because of its ability to classify incomplete cases. Correct classification rates were approximately 10% higher for hoary marmots than for Vancouver Island marmots, a result that could be attributed to different sets of morphological measurements. Zygomatic arch breadth measured in hoary marmots was the most important predictor of age class in both sexes using both classification techniques. We recommend that CART analysis be performed on data-sets with incomplete records and used as a variable screening tool prior to DFA on more complete data-sets.

Plant interactions are unimportant in a subarctic–alpine plant community

“…Regression trees split data in a binary way into progressively more homogenous groups based on the predictor variable at each split that explains the greatest deviance in the data set (Crawley 2002). Variables can be chosen for splits multiple times within a single analysis, which makes regression trees ideal for detecting complex and nonadditive effects (Karels et al 2004). There are no assumptions about data distribution, and the trees are not influenced by missing data, outliers, or monotonic transformations of the descriptor variables (Breiman et al 1984, De'ath andFabricius 2000).…”

“…There are no assumptions about data distribution, and the trees are not influenced by missing data, outliers, or monotonic transformations of the descriptor variables (Breiman et al 1984, De'ath andFabricius 2000). They are ideal for complex ecological data and are equal to or more effective than multiple regression (De'ath and Fabricius 2000), logistic regression (Vayssieres et al 2000), and discriminant function analysis (Karels et al 2004).…”

Bayesian model-based age classification using small mammal body mass and capture dates

“…For instance, Karels et al (2004) estimated age classification accuracies for marmots (Marmota vancouverensis and M. caligata) between 69% and 86% (mean = 81%) using 2 classification techniques-discriminant function analysis (DFA) and classification and regression trees (CART). Although their results cannot be contrasted directly with ours due to our use of a different study species, age classification schemes, and methods to evaluate accuracy, our model appears to compare favorably with these techniques, especially considering that we only used data on mass, sex, and capture date, as opposed to a suite of 4-8 morphometric variables (see table 1 in Karels et al 2004). While our application in Allegheny woodrats produced accurate classifications using only body mass and sex, additional morphometric variables could easily be added to the analysis, either as a series of univariate models (each of which would depend on a common imputed value for α i ) or by replacing equation 1 with a multivariate mixture distribution.…”

“…While our application in Allegheny woodrats produced accurate classifications using only body mass and sex, additional morphometric variables could easily be added to the analysis, either as a series of univariate models (each of which would depend on a common imputed value for α i ) or by replacing equation 1 with a multivariate mixture distribution. To illustrate, we have reanalyzed the marmot data from Karels et al (2004) in Supplementary Data SD2.…”

“…Correct age classifications for individual study animals are necessary to describe age-specific patterns of survival, to quantify recruitment rates, and to differentiate parental and offspring cohorts for parentage analyses. To identify age in mammals, biologists may focus trapping efforts during seasons when juveniles can be easily differentiated based on body size or pelage, collect precise measurements of body size (Karels et al 2004), assess the progression of tooth eruption or wear (Spinage 1973), or visualize cementum annuli in the cross section of extracted teeth (Hamlin et al 2000). However, the applicability of these and other methods for age identification may vary considerably among species and study systems.…”