Keywords
tree size variables
spatial distribution
G statistic
geographically weighted regression
How to Cite
##article.highlights##
- The phenotypic traits of Pinus arizonica were modeled to determine their behavior and distribution.
- Phenotypic traits do not present a random spatial pattern, but tend to cluster.
- Cone production of Pinus arizonica is related to crown diameter.
- This geospatial approach improved the understanding of clonal orchard dynamics.
Abstract
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.References
Aguirre, O., Hui, G., von Gadow, K., & Jiménez, J. (2003). An analysis of spatial forest structure using neighbourhood-based variables. Forest Ecology and Management, 183(1-3), 137–145. doi: https://doi.org/10.1016/S0378-1127(03)00102-6
Ávila-Flores, D., & Pompa-García, M. (2008). Caracterización de la reproducción clonal del género Pinus en Silvicultores Unidos de Guachochi, Chihuahua. Universale Forestum, 7(2), 12–15. Retrieved from https://www.ujed.mx/doc/publicaciones/universale-forestum/universale-forestum-7-2.pdf
Bozali, N., Sivrikaya, F., & Akay, A. E. (2015). Use of spatial pattern analysis to assess forest cover changes in the Mediterranean region of Turkey. Journal of Forest Research, 20(4), 365–374. doi: https://doi.org/10.1007/s10310-015-0493-2
Bustamante-García, V., Prieto-Ruíz, J. Á., Merlín-Bermudes, E., Álvarez-Zagoya, R., Carrillo-Parra, A., & Hernández-Díaz, J. C. (2012). Potencial y eficiencia de producción de semilla de Pinus engelmannii Carr., en tres rodales semilleros del estado de Durango, México. Madera y Bosques, 18(3), 7–21. doi: https://doi.org/10.21829/myb.2012.183355
Brunsdon, C., Fotheringham, S., & Charlton, M. (1998). Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), 431–443. doi: https://doi.org/10.1111/1467-9884.00145
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. doi: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Carroll, M. W., Matthews, D. A., Hiscox, J. A., Elmore, M. J., Pollakis, G., Rambaut, A., & Abdellati, S. (2015). Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West Africa. Nature, 524(7563), 97–101. doi: https://doi.org/10.1038/nature14594
Comisión Nacional Forestal (CONAFOR). (2014). Huertos semilleros. Retrieved November 11, 2018, from http://www.conafor.gob.mx/innovacion_forestal/?p=1005
Corral-Rivas, J. J., Wehenkel, C., Castellanos-Bocaz, H. A., Vargas-Larreta, B., & Diéguez-Aranda, U. (2010). A permutation test of spatial randomness: Application to nearest neighbour indices in forest stands. Journal of Forest Research, 15(4), 218–225. doi: https://doi.org/10.1007/s10310-010-0181-1
Duarte-Cunha, M., Almeida, A. S. D., Cunha, G. M. D., & Souza-Santos, R. (2016). Geographic weighted regression: applicability to epidemiological studies of leprosy. Revista da Sociedade Brasileira de Medicina Tropical, 49(1), 74–82. doi: https://doi.org/10.1590/0037-8682-0307-2015
Elobaid, R. M., Shitan, M., Ibrahim, N. A., Ghani, A. N., & Daud, I. (2009). Evolution of spatial correlation of mean diameter: A case study of trees in natural dipterocarp forest. Journal of Mathematics and Statistics, 5(4), 267–269. doi: https://doi.org/10.3844/jmssp.2009.267.269
Environmental Systems Research Institute (ESRI). (2011). ArcGIS Desktop: Release 10.2. Redlands, CA, USA: Author.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2004). Geographically weighted regression: The analysis of spatially varying relationship. American Journal of Agricultural Economics, 86(2), 554–556. doi: https://doi.org/10.1111/j.0002-9092.2004.600_2.x
Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Geographical and temporal weighted regression (GTWR). Geographical Analysis, 47(4), 431–452. doi: https://doi.org/10.1111/gean.12071
Flood, N. (2014). Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for both top-of-atmosphere and surface reflectance: A study in the Australian ladscape. Remote Sensing, 6(9), 7952–7970. doi: https://doi.org/10.3390/rs6097952
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. doi: https://doi.org/10.1016/S0034-4257(96)00067-3
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206. doi: https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
González-Ávalos, J., García-Moya, E., Vargas-Hernández, J. J., Trinidad-Santos, A., Romero-Manzanares, A., & Cetina-Alcalá, V. M. (2006). Evaluación de la producción y análisis de conos y semillas de Pinus cembroides Zucc. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 12(2), 133–138. Retrieved from https://www.chapingo.mx/revistas/forestales/contenido.php?seccion=numero&id_revista_numero=34
González-Cásares, M., Pompa-García, M., & Camarero, J. J. (2017). Differences in climate–growth relationship indicate diverse drought tolerances among five pine species coexisting in Northwestern Mexico. Trees, 31(2), 531–544. doi: https://doi.org/10.1007/s00468-016-1488-0
Guevara-Hernández, F., Pinto, R., Rodríguez, L. A., Gómez, H., Ortiz, R., Ibrahim, M., & Cruz, G. (2011). Percepciones locales de la degradación de potreros en una comunidad ganadera de Chiapas, México. Revista Cubana de Ciencia Agrícola, 45(3), 311–319. doi: https://doi.org/10.3923/rjbsci.2010.499.507
Gutiérrez-Vázquez, B. N., Cornejo-Oviedo, E. H., Zermeño-González, A., Valencia-Manzo, S., & Mendoza-Villarreal, R. (2010). Conversión de un ensayo de progenies de Pinus greggii var. greggii a huerto semillero mediante eigen-análisis. Bosque, 31(1), 45–52. doi: https://doi.org/10.4067/S0717-92002010000100006
Lado-Sestayo, R., & Vivel-Búa, M. (2016). Emprendimiento en el ámbito turístico: dinámica y geografía en la industria hotelera. Revista Turydes: Turismo y Desarrollo, 20, 433–444. Retrieved from http://www.eumed.net/rev/turydes/20/hotel.html
Mead, D. J. (2013). Sustainable management of Pinus radiata plantations. Roma: Food and Agriculture Organization of the United Nations. Retrieved from http://www.fao.org/3/a-i3274e.pdf
Moreno-Serrano, R., & Vayá-Valcarce, E. (2002). Econometría espacial: nuevas técnicas para el análisis regional. Una aplicación a las regiones europeas. Investigaciones Regionales, 1, 83–106. Retrieved from http://www.redalyc.org/articulo.oa?id=28900104
Muñoz-Flores, H. J., Prieto-Ruiz, J. Á., Flores-García, A., Pineda-Ojeda, T., & Morales-González, E. (2013). Técnicas de injertado "enchapado lateral" y "fisura terminal" en Pinus pseudostrobus Lindl. Durango, Dgo.: Instituto Nacional de Investigaciones, Agrícolas y Pecuarias. Retrieved from http://biblioteca.inifap.gob.mx:8080/jspui/bitstream/handle/123456789/3977/CIRNOC_010208142000054177ok.pdf?sequence=1
Muñoz-Gutiérrez, L., Vargas-Hernández, J. J., López-Upton, J., Ramírez-Herrera, C., Jímenez-Casas, M., Aldrete, A., & Díaz-Ruíz, R. (2017). Variación espacial y temporal en la dispersión de polen en un huerto semillero y en rodales naturales cercanos de Pinus patula. Bosque, 38(1), 169–181. doi: https://doi.org/10.4067/S0717-92002017000100017
Nakai, A., & Kisanuki, H. (2007). Effect of inundation duration on Salix gracilistyla density and size on a gravel bar. Journal of Forest Research, 12(5), 365–370. doi: https://doi.org/10.1007/s10310-007-0027-7
Pérez, A., Pompa-García, M., Solís, R., & Chacón, J. (2014). Dinámica dasométrica del huerto clonal de Pinus arizonica en los bosques del sur de Chihuahua. In M. Ramos, & V. Aguilera (Eds.), Ciencias Agropecuarias Handbook T-II (pp. 104–112). México: ECORFAN. Retrieved from https://dialnet.unirioja.es/servlet/articulo?codigo=4866119
Pérez-Valbuena, G. J. (2017). Dimensión espacial de la pobreza en Colombia. In J. A. Bonet-Morón (Ed.), Geografía económica y análisis espacial en Colombia (pp. 175–222). Colombia: Banco de la República de Colombia. Retrieved from https://econpapers.repec.org/bookchap/bdrbdrlib/2008-01.htm
Pompa-García, M., & Hernández-González, P. (2012). Determinación de la tendencia espacial de los puntos de calor como estrategia para monitorear los incendios forestales en Durango, México. Bosque, 33(1), 63–68. doi: https://doi.org/10.4067/S0717-9200201200010000
Pimentel, R., Lopes, D. J. H., Mexia, A. M. M., & Mumford, J. D. (2016). Validation of a geographic weighted regression analysis as a tool for area-wide integrated pest management programs for Ceratitis capitata Wiedemann (Diptera: Tephritidae) on Terceira Island, Azores. International Journal of Pest Management, 63(2), 172–184. doi: https://doi.org/10.1080/09670874.2016.1256512
Rendón, C. H., Martínez, Y. A., & Pérez, S. D. (2014). Los bosques, sus bienes y servicios: los retos del manejo forestal sustentable. Ciencias, 111-112, 29–35. Retrieved from http://www.revistaciencias.unam.mx/es/161-revistas/revista-ciencias-111-112/1397-los-bosques,-sus-bienes-y-servicios-los-retos-del-manejo-forestal-sustentable.html
Rodrigues, M., & de la Riva, J. (2014). Assessing the effect on fire risk modelling of the uncertainty in the location and cause of forest fire. Coimbra, PT: Coimbra University Press. Retrieved from http://hdl.handle.net/10316.2/34305
Rodríguez-Laguna, R., Razo-Zárate, R., Juárez-Muñoz, J., Capulín-Grande, J., & Soto-Gutiérrez, R. (2012). Tamaño de cono y semilla en procedencias de Pinus greggii Engelm. var. greggii establecidas en diferentes suelos. Revista Fitotecnia Mexicana, 35(4), 289–298. Retrieved from http://www.scielo.org.mx/scielo.php?pid=S0187-73802012000400005&script=sci_arttext
Rubio-Camacho, E. A., González-Tagle, M. A., Himmelsbach, W., Ávila-Flores, D. Y., Alanís-Rodríguez, E., & Jiménez-Pérez, J. (2017). Patrones de distribución espacial del arbolado en un bosque mixto de pino-encino del noreste de México. Revista Mexicana de Biodiversidad, 88(1), 113–121. doi: https://doi.org/10.1016/j.rmb.2017.01.015
Silva-Flores, R., Pérez-Verdín, G., & Wehenkel, C. (2014). Patterns of tree species diversity in relation to climatic factors on the Sierra Madre Occidental, Mexico. PloS ONE, 9(8), e105034. doi: https://doi.org/10.1371/journal.pone.0105034
Viveros-Viveros, H., Camarillo-Luna, A. R., Sáenz-Romero, C., & Aparicio-Rentería, A. (2013). Variación altitudinal en caracteres morfológicos de Pinus patula en el estado de Oaxaca (México) y su uso en la zonificación. Bosque, 34(2), 173–179. doi: https://doi.org/10.4067/S0717-92002013000200006
Zúñiga-Vásquez J. M., Cisneros-González D., Pompa-García M., Rodríguez-Trejo D. A., Pérez-Verdín G. (2017). Spatial modelling of forest fires in Mexico: an integration of two date sources. Bosque, 38(3), 563–574. doi: https://doi.org/10.4067/S0717-92002017000300014
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