Recruitment Patterns in a Mediterranean Oak Forest: A Case Study Showing the Importance of the Spatial Component

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The spatial patterns of recruits in a Mediterranean forest in Southwest Portugal were examined. We aimed to investigate how several environmental and canopy variables contribute to the observed patterns, and how relevant the spatial component is in explaining the variance in the density of recruits. We took a census of every recruit of four dominant woody species (Arbutus unedo, Quercus faginea, Q. suber, and Viburnum tinus), mapping their position inside two forest plots. For a total of 309 recruits, we measured the following variables: soil moisture, slope, canopy density, herbaceous and litter ground cover, overlying species and height, and distance to the nearest adult of the same and different species. Spatial pattern analysis, principal coordinate analysis of neighbor matrices, canonical correspondence analysis, and variance partitioning were performed to detect significant deviations from complete spatial randomness and identify conspecific and interspecific patterns, characterize regeneration niches, and evaluate the importance of the spatial component. Results showed the existence of significant community structure at the recruitment stage. The distance between recruits and the nearest conspecific adult was the main explanatory variable. Light availability, soil moisture, litter accumulation, and the overlying species were also significant factors in explaining the variance in the density of recruits. The best model accounted for 37% of the overall variance in the density of recruits, and most (80%) of the explained variance corresponded to spatially structured variance. This case study shows the importance of the spatial component in understanding the forest regeneration patterns under Mediterranean conditions and will contribute to the implementation of ecologically based management actions to preserve the remaining forest fragments.

Keywords: J function; Quercus faginea; Quercus suber; principal coordinate analysis of neighbor matrices (PCNM); variance partitioning

Document Type: Research Article

Publication date: December 1, 2007

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