Aho, K. (corresponding author, firstname.lastname@example.org) & Weaver, T. (email@example.com): Department of Ecology, Montana State University, Bozeman, MT 59717-3460, USA Regele, S. (firstname.lastname@example.org): R & R Consulting, Billings, MT, USA Aho, K.: Current address: Department of Biology, Idaho State University, Pocatello, ID 83209-8007, USA
Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods
Article first published online: 4 NOV 2010
© 2010 International Association for Vegetation Science
Applied Vegetation Science
Volume 14, Issue 2, pages 277–290, April 2011
How to Cite
Aho, K., Weaver, T. and Regele, S. (2011), Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods. Applied Vegetation Science, 14: 277–290. doi: 10.1111/j.1654-109X.2010.01110.x
Co-ordinating Editor: Janos Podani
- Issue published online: 1 MAR 2011
- Article first published online: 4 NOV 2010
- Received 23 June 2009;, Accepted 8 September 2010.
- Native community environments;
- Native community types (grassland;
- Pruning analysis;
- Seed mixes;
- Statistical methods for reclamation
Question: How does one best choose native vegetation types and site them in reclamation of disturbed sites ranging from cropland and strip mines?
Application: World-wide, demonstrated in SE Montana.
Methods: We assumed that pre-disturbance native communities are the best targets for revegetation, and that the environmental facet each occupies naturally provides its optimal habitat. Given this assumption, we used pre-strip-mine data (800 points from a 88 km2 site) to demonstrate statistical methods for identifying native communities, describing them, and determining their environments.
Results and conclusions: Classification and pruning analysis provided an objective method for choosing the number of target community types to be used in reclamation. The composition of eight target types, identified with these analyses, was described with a relevé table to provide a species list, target cover levels and support the choice of species to be seeded.
As a basis for siting communities, we modeled community presence as a function of topography, slope/aspect, and substrate. Logistic GLMs identified the optimal environment for each community. Classification and Regression Tree (CART) analysis identified the most probable community in each environmental facet. Topography and slope were generally the best predictors in these models. Because our analyses relate native vegetation to undisturbed environments, our results may apply best to sites with minimal substrate disturbance (i.e. better to abandoned cropland than to strip-mined sites).