Establecimiento de cuatro especies de Quercus en el sur de la ...

Capítulo 3
distribution were developed, residuals of all models (including both regression and
ANOVA ones) were checked for autocorrelation by building spatial correlograms but no
significant patterns were found, and thus independence could be safely assumed (Keitt
et al., 2002; Lichstein et al., 2002). Non spatial statistical analyses were done with the
software STATISTICA 8.0. (Statsoft, Inc.) and R version 2.9.1 (R Development Core
Team 2009).
RESULTS
Spatial aggregation in environmental factors
Most of environmental variables studied (light availability, soil moisture at
different times and herb production) changed according to an aggregated spatial
pattern (9 of the 12 possible cases presented spatial aggregation, Table 2). Herb
production in plot 1 exhibited the highest aggregation index (I a , Table 2). The clustering
index maps produced (Fig. 1) showed patches and gaps (zones with υ greater and less
than 1.5, respectively) for some selected variables. Although the overall pattern was of
the random type in some cases (e.g. maximum spring moisture in plot 1), there were
zones of significant local aggregation.
Figure 1. Maps of clustering indices (υ) for selected variables in each sampled plot. The zones bounded
by solid lines are patches with high values (υ > 1.5) in the variable concerned and those bound by
dashed lines gaps of low values in the variable (υ > 1.5).
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