Hypotheses are frequently posed that require comparisons among diet samples. Hierarchical cluster analysis is suited to this task, but has received little attention in food habits research. By grouping samples so that similar diets are close together and dissimilar diets are spaced farther apart, hierarchical cluster analysis reveals patterns in data difficult to recognize in the matrix of similarity coefficients typical of most food habits studies. We provide an example of this type of analysis, and indicate its application to management of large herbivores. This material was digitized as part of a cooperative project between the Society for Range Management and the University of Arizona Libraries. The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact lbry-journals@email.arizona.edu for further information. Migrated from OJS platform August 2020
Scholarly peer-reviewed articles published by the Society for Range Management. Access articles on a rolling-window basis from vol. 1, 1948 up to 5 years from the current year. Formerly Journal of Range Management (JRM). More recent content is available by subscription from SRM.