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

Spatial and temporal heterogeneity effects on seedling growth and establishment in four Quercus species
died or failed to emerge at the time of measurement to 4 when all four seedlings were
alive. The aggregation pattern was examined separately on the three sampling dates.
As no spatial aggregation was detected in all growth and morphology variables
(except from four cases from 72, see results), these data were assumed to be spaceindependent.
Thus, morphology variables (Stem height and leaf area at the beginning
of the growing season, aboveground biomass and LMF a at the end of the growing
season, RGR t and RGR a ) were compared across species and between the two plots by a
two way ANOVA (plot and species as categorical factors). When necessary, a
logarithmic transformation of the data was made to fulfill the requirements of
normality and variance homogeneity. A non‐parametric Kruskal–Wallis test was
applied in those cases where the transformed data failed to fulfill the ANOVA
assumptions (leaf area and RGR t , first for each species separately to compare variables
between plots and second for each plot separately to make comparisons across
species.
A multiple regression analysis was applied in order to identify the factors
influencing growth and morphology at each species and plot. Seed mass, herb
production, mean soil moisture of the growing season and light availability were used
as independent variables. As emergence time was not correlated to any of the
dependent variables and did not improve any model adjustment, and in order to not
over‐parameterize the models, this factor was not included in the analysis.,
Morphological variables were used as dependent variables.
Q. ilex and Q. suber leaf area was log transformed in order to fulfil regression
requests. For RGR a and RGR t, (x + 1) values were used in the analysis in order to avoid
negative values. For each regression a subset of predictors was selected using a
forward stepwise method. Model fit was evaluated using the adjusted R 2 , which
accounts for different degrees of freedom and, hence, the extra regression
parameters. On each model, error terms and y‐values were normality distributed and
had constant variance. Absence of collinearity across dependent variables was checked
as the variance inflation factor (FIV) that was in all cases below two (Graham, 2003).
Although models for those dependent variables that presented aggregates spatial
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