Fine‐scale spatial structure in a grassland community: quantifying the plant’s‐eye view

Abstract

Summary 1The fine-scale spatial patterns of Agrostis stolonifera , Holcus lanatus and Lolium perenne were recorded in an English lowland grassland as presence/absence maps from 400-cell quadrats at two different scales (2 × 2 cm or 8 × 8 cm cells). 2 Local spatial structure in these patterns was quantified using spatial covariance functions. Distance-and direction-dependent components were examined separately for both intra-and interspecific patterns. The significance of departures from randomness was determined using Monte Carlo techniques. 3 The smaller-scale data showed that all three species were significantly aggregated, Agrostis to a greater distance (8 cm) than Holcus or Lolium ( 4 cm). The intensity of aggregation decreased in the order Lolium > Holcus > Agrostis . The larger-scale data suggested that this aggregation extended to greater distances, and that it was most intense in Agrostis . 4 Despite the lack of visual directionality in the environment, Agrostis showed a directional pattern at both scales, with Lolium varying in the same direction at the larger scale. 5 Only Agrostis and Lolium showed a significant interspecific relationship (segregated to 2 cm at the small scale, but aggregated to 8 cm at the larger scale). There was no evidence of directionality in the interspecific components of pattern. 6 The nature of spatial structure appears to depend on the scale of observation, but the smaller-scale data are more likely to provide a biologically interpretable measure of local spatial structure in this community.

Dispersal syndromes related to edge distance in cerrado sensu stricto fragments of central-western Brazil

“…For instance, myrmecochorous species may be grouped as poor dispersers, whereas epizoo-and endozoochorous (except for low understorey herbs) may be grouped as good dispersers (Svenning and Skov, 2002). The observed spatial patterns may reflect that communities show fine-scale structure above and beyond that generated by local dispersal (Purves and Law, 2002). It means that certain species would be more strongly associated than others and some of this variation could be attributed to plant traits; species with more disparate seed or plant sizes would have more negative associations (Turnbull et al, 2004).…”

“…It means that certain species would be more strongly associated than others and some of this variation could be attributed to plant traits; species with more disparate seed or plant sizes would have more negative associations (Turnbull et al, 2004). Vegetative propagation, very common in the cerrado (Rizzini, 1965;Hoffmann, 1998), could be another cause of plant aggregation (Purves and Law, 2002), but the change of the reproductive mode from propagation via seeds to vegetative depending on local circumstances (Prati and Schmid, 2000;Kanno and Seiwa, 2004;Gros et al, 2006) in cerrado patches remains to be investigated. We made no measurements of seed dispersal, seed deposition, or environmental conditions; even if empirical support was lacking, our evidences may be regarded as patterns generating postulates and hypotheses (Levine and Murrell, 2003).…”

Shrubs as foundation species in a high tropical alpine ecosystem: a multi-scale analysis of plant spatial interactions

“…The expected value for these functions under complete spatial randomness is 1; values >1 indicate spatial aggregation at a given distance, while values <1 indicate segregation (see Purves andLaw 2002 andLlambí et al 2004). For analysing the spatial distribution of H. laricifolium (small and large individuals independently) we calculated auto-covariance functions.…”

Climate variability has a stabilizing effect on the coexistence of prairie grasses

“…To initialize a simulation grid, we first specified cover for each species, then calculated variogram model parameters from the quadratic functions, and finally, used these parameters within indicator simulation and assigned states to all cells in the grid. Maps generated by sequential indicator simulation reproduce (within statistical f luctuations) the specified cover and variogram model for each species (29) and thus constitute realistic initial conditions. Climate Correlations.…”

“…These transitions depend on both survival (of occupied cells) and colonization. The central assumption of the model is that cell transitions depend only on the previous state of each cell and the composition of its local neighborhood (28,29). To limit edge effects, we discarded all focal cells within 10 cm of the quadrat border.…”