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

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Capítulo 3<br />

July mea<strong>sur</strong>em<strong>en</strong>ts were ma<strong>de</strong> with 7 cm rods since the soil was too dry for the longer<br />

ones. Herbaceous aboveground biomass was harvested in July 2007 within a 25 × 25<br />

cm square c<strong>en</strong>tred at each nodal point and ov<strong>en</strong>‐dried in a stove at 70°C for at least 48<br />

h prior to <strong>de</strong>termine dry weight.<br />

Statistical analysis<br />

The spatial pattern for the studied variables was examined by Spatial Analysis by<br />

Distance Indices (SADIE) (Perry 1998) as implem<strong>en</strong>ted in SadieSh<strong>el</strong>l v 1.3<br />

(www.rothamsted.ac.uk/pie/sadie). The spatial pattern for each factor was assessed in<br />

terms of the aggregation in<strong>de</strong>x (I a ) and clustering in<strong>de</strong>x (υ). I a is a mea<strong>sur</strong>e of global<br />

aggregation in the variable concerned; <strong>de</strong>p<strong>en</strong>ding on whether I a is unity, greater than<br />

unity or less than unity, the pattern is of the random, aggregate or regu<strong>la</strong>r type,<br />

respectiv<strong>el</strong>y (Maestre, 2003). Clustering in<strong>de</strong>x (υ) quantifies the contribution of each<br />

sampling unit to the overall spatial pattern; because is a continuous variable, it can be<br />

used for linear interpo<strong>la</strong>tion (Leg<strong>en</strong>dre and Leg<strong>en</strong>dre, 1998) in or<strong>de</strong>r to obtain maps<br />

clearly showing patches (zones with υ > 1.5) and gaps (υ < 1.5).<br />

A spatial covariance analysis was performed in or<strong>de</strong>r to i<strong>de</strong>ntify any coinci<strong>de</strong>nces<br />

in space betwe<strong>en</strong> aggregated zones for the differ<strong>en</strong>t variables (Perry and Dixon 2002).<br />

SADIE mea<strong>sur</strong>em<strong>en</strong>ts provi<strong>de</strong>d an overall clustering in<strong>de</strong>x X (<strong>de</strong>gree of spatial<br />

coinci<strong>de</strong>nce betwe<strong>en</strong> two variables) ranging from –1 (dissociation) to 1 (association). In<br />

addition, they provi<strong>de</strong>d a local clustering in<strong>de</strong>x χ which estimates the contribution of<br />

each sampling unit to the overall clustering pattern. Like the clustering in<strong>de</strong>x (υ), these<br />

two indices are continuous and afford mapping in or<strong>de</strong>r to better <strong>en</strong>visage zones of<br />

association and dissociation betwe<strong>en</strong> pairs of variables. In this work, maps were<br />

produced by linear interpo<strong>la</strong>tion with the software SURFER v. 8 (Gol<strong>de</strong>n Software,<br />

Inc.).<br />

Data were not subjected to spatial analysis in those cases where the number of<br />

living p<strong>la</strong>nts was less than 10. The spatial distribution of <strong>sur</strong>vival and establishm<strong>en</strong>t<br />

success for each species was studied at three points in time during the dry period<br />

(July–September). Establishm<strong>en</strong>t success was also assessed at each sampling point for<br />

all species in combination; this variable therefore ranged from 0 wh<strong>en</strong> all seedlings had<br />

81

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