Small‐scale spatial autocorrelation in plant communities: the effects of spatial grain and measure of abundance, with an improved sampling scheme

Abstract

Questions How does the spatial structure of plant communities vary with the spatial grain and with the measure of species presence used? How can communities most efficiently be sampled for spatial autocorrelation? Location Four communities – riverbed, bog, ultramafic shrubland/herbfield and forest – in southwest New Zealand. Methods Each site was sampled over an extent of ca. 120 m at seven spatial grains, from 0.0025 to 25 m2, using an innovative triangular sampling scheme. At the 1‐m2 grain, species abundances (local frequencies) were recorded, as well as presence/absence. Results The percentage variation in species composition explained by distance, i.e. by spatial autocorrelation, was higher at larger grain. However, it reached a maximum of only 15%. The nugget – the Y‐intercept of the dissimilarity/distance relation – has been seen as a measure of randomness in community composition. It was generally about 0.5 dissimilarity on a 0–1 scale, although values in the range 0.7–0.8 were found at smaller grain sizes in the forest. The 90% distance, i.e. the distance at which dissimilarity reaches 90% of its final value, was interpretable only for the two sites where spatial autocorrelation was strong, but gave realistic estimates. Unsurprisingly, some parameter estimates were unrealistic when the fits were poor. Abundance information added nothing to the ability of distance to predict dissimilarity. Conclusion The strength of spatial autocorrelation rose with increasing grain, to a low value but one congruent with the few comparable studies in the literature. That is, control of species composition seemed to be at the larger grain sizes sampled, rather than at a very fine scale. Strong spatial autocorrelation has been reported only over large extents, over environmental heterogeneity and/or when examining one guild within a community. The nugget was generally somewhat lower than other values in the literature, indicating less randomness. The lack of increased spatial community predictability when including species abundances conforms to the majority of previous studies, suggesting that the primary community control is on the presence of species, not their abundance. However, the differences in spatial autocorrelation between the four sites sampled emphasize that comparative studies using consistent methods are needed. The triangular sampling scheme used here was rapid, accurate, and efficient in its distribution of distances.

Complex spatial clonal structure in the macroalgae Fucus radicans with both sexual and asexual recruitment

“…We used spatial autocorrelation analysis (Sokal and Wartenberg 1983;Smouse and Peakall 1999) to study the effects of fragment dispersal and spatial genetic structure in three of the populations. This has previously been performed in terrestrial plant populations (Epperson and Chung 2001;Vekemans and Hardy 2004;Gapare and Aitken 2005;Roe et al 2011) and also in marine seagrasses (Reusch et al 1999;Ruggiero et al 2005;Zipperle et al 2011). The method examines the genetic relatedness between pairs of individuals with regard to their relative positions in space (Alberto et al 2005) and estimates clonal subranges (local areas of distribution of the same clone) and spatial scales over which clonal processes still appear to affect the local genetic structure of the population.…”

Chance in plant communities: a new approach to its measurement using the nugget from spatial autocorrelation

“…Some have plotted similarity or dissimilarity against the log of distance, which makes the nugget impossible to define (Soininen, McDonald & Hillebrand 2007). Others have sampled only one guild of the flora, such as ferns, palms, woody Fabaceae or Melastomataceae, or native and exotic species separately but not in total (see Wilson & Meurk 2011;Roe et al 2012). Fortunately most of the comparable studies have used, like us, the Jaccard measure of similarity or dissimilarity.…”

“…Alternatively, the species composition at small grain might be less predictable because quadrats would less reliably include sparse species, at least at one point in time (Bellehumeur, Legendre & Marcotte 1997;Nekola & White 1999). Roe et al (2012), sampling the plant communities of four sites at a range of spatial grains, found weaker spatial autocorrelation at smaller grain. The trend with grain is opposite in the present results, but for 12 of the 16 sites the nugget was larger at small spatial grain (but binomial P = 0.077).…”

Decay of ecosystem differences and decoupling of tree community–soil environment relationships at ecotones

“…Spatial structure in tree community composition that was independent of soil properties was not detected unless ecotones were included in the analysis. Although spatial clustering indicative of dispersal limitation can often be detected in mapped stem data sets (Wiegand et al 2007, other studies that use small forest plots similar to ours typically detect low levels of spatial structure in plant community composition (Gilbert and Lechowicz 2004, Roe et al 2012, Shipley et al 2012. These results indicate that, in the absence of strong environmental gradients such as at ecotones, most tree species are distributed widely enough relative to seed dispersal kernels that dispersal limitation is not very strong except at very small spatial scales.…”

“…Metacommunity paradigms, including mass effects, are often conceptualized by considering the interactions among discrete habitat patches (Leibold et al 2004). In continuous habitat types such as forest, it is often assumed that dispersal limitation will be most evident in environmentally homogeneous areas (Caruso et al 2012, Roe et al 2012). However, ecotones may actually provide a better opportunity to study dispersal limitation and mass effects in these areas because of the potential decoupling of strong community-environment relationships.…”