A comparison of modelling techniques for small mammal diversity

Development pressures frequently dictate that managers' need to make decisi ons about which local sites will be developed and which will be protected. When management for diversity is the goal, it would be helpful if models co uld aid these decisions. We compared three methods for modelling site-speci fic small mammal diversity at 48 0.58-ha study sites distributed within six habitats in foothills of the Sacramento Mountains, south-central New Mexic o, spring and fall, 1993-1994. Methods included; 1) direct richness predict ion with discriminant analysis (classification success rate of 15.1%, mean error = 1.6 species), 2) prediction of richness based upon expected species -specific habitat suitability with discriminant analysis (classification su ccess rate 20.3%, mean error = 1.6 species), and 3) prediction of relative richness thigh vs, low) (classification success rate = 91.1%). The mean err or of methods 1 and 2 (1.6 species) exceeds the difference known to disting uish high richness habitats from low (1.3 species) in this ecosystem. There fore, we conclude that the appropriate conceptual technique for modelling d iversity is to proceed by distinguishing high and low diversity habitats. W e found this technique preferable when compared to pursuit of error-prone m odels for actual richness that have mean errors larger than those known to characterize the system. (C) 1999 Elsevier Science B.V. All rights reserved .