Jayne L. Jonas, Deborah A. Buhl, and Amy J. Symstad. 2015. Impacts of weather on long-term patterns of plant richness and diversity vary with location and management. Ecology 96:2417–2432. http://dx.doi.org/10.1890/14-1989.1
Complete AICc results for the College Pasture site (Hays, KS, USA).
Ecological Archives E096-213-S6.
File list (downloads)
Jayne L. Jonas,1,4,5 Deborah A. Buhl,2 and Amy J. Symstad3
1IAP World Service, Inc.
1739 Fletcher Ave
Lochbuie, CO 80603 USA
2U.S. Geological Survey, Northern Prairie Wildlife Research Center
8711 37th Street SE
Jamestown, ND 58401 USA
3U.S. Geological Survey, Northern Prairie Wildlife Research Center
Wind Cave National Park
26611 U.S. Highway 385
Hot Springs, SD 57747 USA
4 Present address: Colorado State University, Department of Forest and Rangeland Stewardship
1472 Campus Delivery
Fort Collins, CO 80523-1472 USA.
5Corresponding author. E-mail: email@example.com
Supplement6.csv (MD5: e3f159b1ef6d3783b663e3c70bbd2371)
Model selection results from analysis of the relationship between weather models and total plant species (A) richness and (B) diversity in each plant assemblage at College Pasture (19341972, N = 39). R² = least-square means regression coefficient, K = # of model parameters including intercept and first order autoregressive parameter, LL = log likelihood, AICc = Akaike Information Criterion corrected for small samples size, DAICc = difference in AICc value between given model and model with lowest AICc, wi = Akaike weights (measure of relative importance of a given model compared to all other models in the model set). Treatments (TRT) are as follows: No_bb = ungrazed big bluestem assemblage, No_et = ungrazed ecotone assemblage, No_lb = ungrazed little bluestem assemblage, No_sg = ungrazed shortgrass assemblage, Yes_sg = cattle-grazed shortgrass assemblage. CV = coefficient of variation, sum = summer, spr = spring, win = winter, PPT = precipitation, TEMP = temperature, t_1 = season preceding growing season (spring and summer) when plants were sampled. Ecologically meaningful models (EMMs) were those with DAICc < 2 and R² > 0.30. Model # corresponds to a priori models as described in Appendix B.