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Revista Chapingo serie ciencias forestales y del ambiente
versión On-line ISSN 2007-4018versión impresa ISSN 2007-3828
Resumen
ALVARADO-BARRERA, Rolando; POMPA-GARCIA, Marín; ZUNIGA-VASQUEZ, José M. y JIMENEZ-CASAS, Marcos. Spatial analysis of phenotypic variables in a clonal orchard of Pinus arizonica Engelm. in northern Mexico. Rev. Chapingo ser. cienc. for. ambient [online]. 2019, vol.25, n.2, pp.185-199. Epub 19-Feb-2021. ISSN 2007-4018. https://doi.org/10.5154/r.rchscfa.2018.11.086.
Introduction:
Seed orchards provide germplasm genetically suitable for use in forest restoration. Knowledge of the spatial distribution of attributes is crucial for their management.
Objective:
To model cone production and tree size variables in a clonal orchard of Pinus arizonica Engelm. from a geospatial perspective in order to determine their behavior and distribution.
Materials and methods:
The spatial pattern of tree size variables and cone production of 126 ramets were determined through a geospatial analysis, using the Getis-Ord G statistic. A Pearson correlation analysis (P ≤ 0.05) determined the variables best associated with cone production and these were examined with stepwise regression. In terms of cone production, the best combination was modeled through a geographically weighted regression.
Results and discussion:
Statistically significant (P < 0.01) clustering values were found in the orchard. Correlation analysis showed that all tree size variables, including the moisture index, were statistically related to cone production. Stepwise regression identified a model that presented crown diameter as the variable that best explained cone production. Geographically weighted regression showed that crown diameter moderately influenced cone production.
Conclusion:
Tree size variables and cone production presented a tendency towards clustering. The use of a geospatial perspective allowed a better understanding of the spatial dynamics of tree size variables.
Palabras llave : cone production; tree size variables; spatial distribution; G statistic; geographically weighted regression.