A new methodology to evaluate natural regeneration: A case study of Quercus ilex in the montado in Portugal

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Abstract
Regeneration and recruitment are primordial for stand sustainability. However, natural regeneration is usually only evaluated by density and height, so no details are provided regarding the individual traits of the saplings/seedlings. Hence, the findings of these regeneration studies are of limited selection power in future management. The main goal of this study was to develop a new methodology that enables the evaluation of individual quality traits of regeneration in the early stages of stand development so that their future potential development can be assessed. Natural regeneration (NR) was evaluated in each field plot using the conjugation of two new methods: first using the natural regeneration density (NRD) method and second using the new natural regeneration tree classification (NRTC). NRTC is composed of a set of criteria with categorical variables that allow the evaluation of traits of individuals and their potential for future recruitment. In this classification system, five criteria were used, namely, upper story influence to evaluate the competition of the trees of the upper story cast on the regeneration individuals, crown isolation to characterize the competition between saplings, height as a surrogate of sapling development, diameter as a proxy of growth, and tree habit as a proxy of the habit of saplings. To test both methods, we applied them in a case study of Quercus ilex in the montado agroforestry system in Portugal, where different degrees of crown cover coexist. Our results indicated that in 85% of the plots with a crown cover higher than 30%, we could find good-quality natural regeneration oak saplings suited to be recruited and located outside of the upper story crown projection. Only 15% of the plots with a crown cover higher than 30% were dominated by Cistus ladanifer and had almost no oak saplings. In future, we suggest that the NRTC be used in all NR individuals in the forest inventory when the NR density is less than 200 NR individuals ha−1.
Funding Information
  • Foundation for Science and Technology

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