Effects of Stress on Community Assemblages in Boreal Forests Andrew Barta, Ellen Hermanson, Matthew Jacobson, Tessa Wirz, Evan Weiher, and Faculty Mentor Todd Wellnitz Introduction Two types of plant community assemblage have been studied: stochastic, or random, assemblage and niche-based assemblage. In plant communities with a high stress filter, niche assembly becomes stronger over stochastic assembly because fewer species are able to tolerate the conditions. Those that can tolerate these conditions are similar in composition and have established their niche as a community in the ecosystem. In low stress communities, stochastic methods of assemblage are stronger than in high stress communities because more species are able to tolerate these mild conditions and are therefore more varied in composition (Chase, 2007). Chase, J.M. “Drought mediates the importance of stochastic community assembly.” PNAS Oct : Vol 104 no. 44 pp Discussion Our results show that an environmental stressor such as slope changes the strength of niche or stochastic assembly patterns in plant communities. Furthermore, it shows that this relationship is still valid in a natural setting that has assembled over a long time period. Constrained Correspondents Analysis (CCA) allows us to choose specific vectors (represented by blue arrows) to get a better idea of their effect. The direction the arrow is pointing represents a high value (high slope), while the opposite direction represents decreasing values (low slope). The point of the triangle at the head of the slope vector arrow shows the similarity of composition between sites at high slope, with greater variation at low slope. The base of the triangle at the head of the aspect vector arrow shows there is more variation in species on a southern aspect. The point of the triangle further down the aspect arrow shows less variation on a northern aspect. High slope and/or north aspect sites with little variation have more emphasis on niche selection while low slope and/or south sites show more stochastic assembly. Since the soil depth vector is opposite the slope vector this implies an inverse interaction; as slope increases soil depth tends to decrease. Methods Randomized transects were used to generate random plots from which we gathered vegetation data on low and high slope sites. All herbaceous plants within a one meter radius were identified to species, while shrubs, vines and trees were identified to species within a five meter radius from the center of the herbaceous plot. Species abundance was determined by estimating percent coverage within the plot by each species Slope, aspect and soil profiles (at greatest practical depth), were taken and recorded for each plot. Boundary Waters Canoe Area Wilderness, located in northern MN, where our data was collected. Non-metric Multidimensional Scaling (NMDS) shows how similar sites are based on all data collected. The seven sites with the lowest slope are circled in green. These sites have a wide range meaning they are less similar and have more variation. This pattern indicates that stochastic assembly is more important in low slope sites than in high slope sites. The seven sites with the highest slope are circled in red. These sites are much closer together on the graph. This means that they are more similar and have less variation. In high slope sites more emphasis is placed on niche selection than in low slope sites. Box plot showing the differences in variance in the first non-metric multidimensional axis between high and low slope sites. Low slope communities showed greater variance than high slope communities (F = , df = 6, p-value = ). Results These graphs condense multiple dimensions into one 2D graph in which units are not necessary. It is best to imagine it similar to a shadow on the wall and as you rotate your hand, the 2D image on the wall changes. These graphs represent the view of the multiple dimensions that best show the factors that “pull” on the data. Sites are represented by circles and individual species are represented by crosses. Sites that share the most species will be physically closer on the graph than those that share few or no species. Strength of stochastic assemblage over a stress gradient Stress Niche Stochastic Low High Low High