PARENT SESSION
Oral Session # 28: Ecological Modeling II.
Presiding: R Park
Tuesday, August 5. 1:30 PM to 5:00 PM, SITCC Meeting Room 102.
Matrix modelling of natural and pollution-induced forest vegetation gradients.
Anand, Madhur1, Tucker, Brian*,1, 1 Department of Biology, Laurentian University, Sudbury, Ontario, Canada
ABSTRACT- We sought to compare the efficacy of the stationary Markov model and conventional ordination techniques in describing compositional and structural changes in forest vascular plant communities along natural and manmade spatial gradients at two scales, local and regional. Stationary Markov models have been used extensively in ecology to study community change with time but estimation of transition matrix probabilities in the absence of individual-based data has been problematic. We applied a method of estimating species transitions for the Markov model put forward to deal specifically with coenosere ecological data. Vegetation abundance (understory percent cover, overstory frequency) and structure (overstory height) data are from six sites spanning a pollution gradient in the Great Lakes-St. Lawrence forests near Sudbury, Ontario, Canada. The manmade gradient varies from barren locations near smelter stacks to diverse, minimally impacted forests 40 km away. At each site, parallel transects extend through suspected natural spatial gradients down south-facing slopes where topographic factors were thought to affect plant communities. Both ordination techniques and the Markov analyses detected the strong regional pollution-induced gradients in abundance and structure. Ordination did not detect slope-related local gradients despite the general trend that, as pollution-level decreases along the regional gradient, vegetation along the slopes begins to display Markovian spatial dynamics. This is due to information loss resulting from static ordination analyses: information regarding transitions between observations along the natural ordering of quadrats is not maintained.
Key words: perturbation gradient, Great Lakes-St. Lawrence forest, stationary Markov model, ordination