How organisms modify the environment can have important implications for where they grow and the services they provide. Most ecological research has focused on individual or species responses to environmental conditions, conditions often described by averages and variances. However, the role of plants as engineers or modifiers of local environments, particularly in being able to create more predictable conditions, is poorly understood. For instance, if unpredictability is correlated with stress, we expect more stressful conditions on larger plants where temperature, wind and light conditions are harder to predict across the full span of their height. Also, if stress increases the intensity of environmental filters, fewer species will be able to become dominant and should instead remain as non-dominants. Further, the milder understory, moderated by dominant plants, could equalize species differences, and cause neutral or near-neutral performances.
To determine the effect of large and ubiquitous (dominant) plants on other plants, over two years and across five meadows, we removed dominant plants from five 1m2 plots. In other plots, we randomly removed similar amounts of non-dominant species and similar amount of biomass. We used the mean similarity among different subset of plants in the plot as a measure of their predictability.
Results/Conclusions
As expected, preliminary analyses show an increase in similarity (more similarity, implies a more consistent response to the disturbance) mostly in the dominant removal plots, when only the most abundant species that replace the removed dominant plants are considered. This happens despite the fact that these new dominant species were unable to fully occupy the volume taken by the previously dominant plants. The increase in similarity is consistent with a stronger environmental filter acting on dominant plants – regardless the species identity, making the dominant plants more predictable than the non-dominant ones. This also suggests that more unpredictable environmental conditions were correlated with more predictable plant communities.