| Summary: | Identifying the spatial scale at which particular mechanisms influence plant community assembly is crucial to understanding the mechanisms structuring communities. It has long been recognized that many elements of community structure are sensitive to area; however the majority of studies examining patterns of community structure use a single relatively small sampling area. As different assembly mechanisms likely cause patterns at different scales we investigate how plant species co-occurrence patterns change with sampling unit scale. We use the checkerboard score as an index of species segregation, and examine species C-score-sampling area patterns in two ways. First, we show via numerical simulation that the C-score-area relationship is necessarily hump shaped with respect to sample plot area. Second we examine empirical C-score-area relationships in arctic tundra, grassland, boreal forest, and tropical forest communities. The minimum sampling scale where species co-occurrence patterns were significantly different from the null model expectation was at 0.1 m2 in the tundra, 0.2 m2 in grassland, and 0.2 Ha in both the boreal and tropical forests. Species were most segregated in their co-occurrence (maximum C-score) at 0.3 m2 in the tundra (0.54 m by 0.54 m quadrats), 1.5 m2 in the grassland (1.2 by 1.2 m quadrats), 0.26 Ha in the tropical forest (71 m by 71 m quadrats), and a maximum was not reached at the largest sampling scale of 1.4 Ha in the boreal forest. The most important finding is that the dominant scales of community structure in these systems are large relative to plant body size, and hence we infer that the dominant mechanisms structuring these communities must be at similarly large scales. This provides a method for identifying the spatial scales at which communities are maximally structured; ecologists can use this information to develop hypotheses and experiments to test scale-specific mechanisms that structure communities. Devon - Pit 1 Plant species presence/absence from Pit1 on Devon Island ...
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