Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A spatially explicit, multi-objective framework for optimal seed-tree selection. |
| Συγγραφείς: |
Nanos, Nikos1 (AUTHOR) nikosnanos@for.auth.gr, Gil, Luis2 (AUTHOR) |
| Πηγή: |
Canadian Journal of Forest Research. 2/23/2026, Vol. 56, p1-13. 13p. |
| Θεματικοί όροι: |
*Multi-objective optimization, *Spatial arrangement, *Genetic algorithms, *Seed harvesting, *Pine, *Seed dispersal, *Recruitment (Population biology) |
| Περίληψη: |
Harvest planning in even-aged stands is challenging especially in ecosystems where variable seed production and low seedling survival constrain regeneration success. Conventional seed-tree guidelines prescribing fixed retention densities (e.g., 30 trees·ha−1) often fail to ensure adequate seed supply and favorable microenvironments by overlooking within-stand spatial variability. Here we aim to develop and test a novel, data-driven framework to optimize seed-tree selection. The approach was demonstrated in a mature Pinus canariensis stand (3 ha, 117 stems·ha−1). We integrated spatially explicit models of seed dispersal and seedling recruitment into a multi-objective optimization algorithm seeking to minimize residual stand density, post-harvest seed loss, and tree-to-seedling competition, while promoting spatial homogeneity in post-harvest seed supply and seedling numbers. A genetic algorithm identified an optimal configuration retaining 31 trees·ha−1, strategically positioned to enhance downhill seed dispersal and reduce competitive effects. The optimized plan balanced trade-offs, reducing predicted seed rain by 15% but increasing predicted seedling recruitment by 17%, compared with the pretreatment baseline. This study represents the first data-driven attempt to provide operational guidance for ecologically optimal implementation of the seed-tree method by balancing timber harvest with expected regeneration success. [ABSTRACT FROM AUTHOR] |
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