Enhanced tools for predicting annual stone pine (Pinus pinea L.) cone production at tree and forest scale in Inner Spain
Abstract
Aim of the study: To present a new spatiotemporal model for Pinus pinea L. annual cone production with validity for Spanish Northen Plateau and Central Range regions. The new model aims to deal with detected deficiencies in previous models: temporal shortage, overestimation of cone production on recent years, incompatibility with data from National Forest Inventory, difficulty for upscaling and ignorance of the inhibitory process due to resource depletion.
Area of study: Spanish Northern Plateau and Central Range regions, covering an area where stone pine occupies more than 90,000 ha
Material and methods: Fitting data set include 190 plots and more than 1000 trees were cone production has been annually collected from 1996 to 2014. Models were fitted independently for each region, by means of zero-inflated log normal techniques. Validation of the models was carried out over the annual series of cone production at forest scale.
Results: The spatial and temporal factors influencing cone production are similar in both regions, thus the main regional differences in cone yield are related with differences in the phenological timing, the intensity of the influent factors and forest intrinsic conditions. A significant inhibition of floral induction by resource depletion was detected and included into the model. Upscaling the model results in accurate prediction at forest scale.
Research highlights: [1] The new model for annual cone production surpass the detected deficiencies of previous models, accurately predicting recent decay in cone production; [2] Regional differences in cone production are due to phenological and seasonal climatic differences rather than to between provenances genetic differences
Keywords: zero-inflated models; pine nut; conelet losses; Leptoglossus occidentalis; forest upscaling.
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References
Bonet JA, Palahí M, Colinas C, Pukkala T, Fischer CR, Miina J, Martínez De Aragón J, 2010. Modelling the production and species richness of wild mushrooms in pine forests of Central Pyrenees in northeastern Spain. Can J For Res 40: 347-356. http://dx.doi.org/10.1139/X09-198
Bracalini M, Benedettelli S, Croci F, Terreni P, Tiberi R, Panzavolta T, 2013. Cone and Seed Pests of Pinus pinea: Assessment and Characterization of Damage. For Entomol 106: 229–234.
Bravo F, Alvarez-Gonzalez JG, Del Rio M, Barrio M, Bonet JA, Bravo-Oviedo A, Calama R, Castedo-Dorado F, Crecente-Campo F, Condes S, et al., 2011. Growth and yield models in Spain: historical overview, contemporary examples and perspectives. Forest Systems 20(2): 315-328.
http://dx.doi.org/10.5424/fs/2011202-11512
Calama R, Montero, G, 2007. Cone and seed production from stone pine (Pinus pinea L.) stands in Central Range (Spain). Eur J For Res 126(1): 23-35. http://dx.doi.org/10.1007/s10342-005-0100-8
Calama R, Madrigal G, Candela JA, Montero G, 2007. Effect of fertilization on the production of an edible forest fruit: stone pine (Pinus pinea L.) nuts in SW Andalusia. Forest Systems 16(3): 241-252.
http://dx.doi.org/10.5424/srf/2007163-01013
Calama R, Mutke S, Gordo J, Montero G, 2008. An empirical ecological-type model for predicting stone pine (Pinus pinea L.) cone production in the Northern Plateau (Spain). For Ecol Manage 255 (3/4): 660-673.
Calama R, Tome M, Sánchez-González M, Miina J, Spanos K, Palahi M, 2010. Modelling Non-Wood Forest Products in Europe: a review. Forest Systems 19: 69-85. http://dx.doi.org/10.5424/fs/201019S-9324
Calama R, Mutke S, Tomé JA, Gordo FJ, Montero G, Tomé M, 2011. Modelling spatial and temporal variability in a zero-inflated variable: the case of stone pine (Pinus pinea L.) cone production. Ecol Model 222: 606-618. http://dx.doi.org/10.1016/j.ecolmodel.2010.09.020
Calama R, Manso R, Tomé JA, 2012. ¿Cómo modelizar datos con exceso de ceros? Métodos y aplicaciones a la investigación forestal. Cuadernos SECF 34: 55-65.
Calama R, Pardos M, Conde M, Madrigal G, Mutke S, Montero G, Gordo FJ, 2015. Pérdidas de rendimiento en piñón en piñas de Pinus pinea L.: análisis interregional. III Reunión Científica de Sanidad Forestal SECF. Madrid 7-8 octubre 2015.
Cañadas MN, 2000. Pinus pinea L. en el Sistema Central (valles del Tiétar y del Alberche): desarrollo de un modelo de crecimiento y producción de piña. Ph D Thesis. Universidad Politécnica, Madrid.
Castellani C, 1989. La produzione legnosa e del fruto e la durata economico delle pinete coetanee di pino domestico (Pinus pinea L.) in un complesso assestato a prevalente funzione produttiva in Italia. Annali ISAFA 12: 161-221.
Crone E, Miller E, Sala A, 2009. How do plants know when other plants are flowering? Resource depletion, pollen limitation and mast-seeding in a perennial wildflower. Ecology Letters 12: 1119-1126. http://dx.doi.org/10.1111/j.1461-0248.2009.01365.x
Finat L, Gordo J, 2014. Sensibilización acerca del papel de la propiedad pública y gestión forestal sostenible en la provincia de Valladolid. Jornada proyecto PROPINEA, Pedrajas de san Esteban, noviembre 2014. http://propinea.es/wp-content/uploads/Sensibilizacion-acerca-del-papel-de-la-propiedad-publica.pdf
García Güemes C, 1999. Modelo de simulación selvícola para Pinus pinea L. en la provincia de Valladolid. Ph D Thesis. Universidad Politécnica, Madrid.
Gordo FJ, 2004. Selección de grandes productores de fruto de Pinus pinea L. en la Meseta Norte. Ph D Thesis. Universidad Politécnica, Madrid.
Isagi Y, Sugimura K, Sumida A, Ito H, 1997. How does masting happen and synchronize? J Theor Biol 187: 231–239. http://dx.doi.org/10.1006/jtbi.1997.0442
Knops JMH, Koenig WD, Carmen WJ, 2007. A negative correlation does not imply a trade-off between growth and reproduction in California oaks. PNAS (USA) 104: 16982-16985. http://dx.doi.org/10.1073/pnas.0704251104
Koenig WD, Knops JMH, 2013. Large-scale spatial synchrony and cross-synchrony in acorn production by two California oaks. Ecology 94(1): 83–93. http://dx.doi.org/10.1890/12-0940.1
Lambert D, 1992. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34 : 1–14. http://dx.doi.org/10.2307/1269547
Lesieur V, Yart A, Guilbon S, Lorme P, Auger-Rozenberg MA, Roques A, 2014. The invasive Leptoglossus seed bug, a threat for commercial seed crops, but for conifer diversity?. Biol Invassions 16(9): 1833–1849. http://dx.doi.org/10.1007/s10530-013-0630-9
Mandallaz D, 2007. Sampling techniques for Forest inventories. Chapman & Hall/CRC. Applied Environmental Statistics. Boca Ratón, 256 pp. http://dx.doi.org/10.1201/9781584889779
Montero G, Cañadas N, Yagüe S, Bachiller A, Calama R, Garriga E, Cañellas I, 2003. Aportaciones al conocimiento de las masas de Pinus pinea L. en los Montes de Hoyo de Pinares (Ávila – España). Revista Montes 73.
Montero G, Calama R, Ruiz Peinado R, 2008. Selvicultura de Pinus Pinea L. En Montero G, Serrada R, Reque J (Eds.) Compendio de Selvicultura de Especies, pp 431-470. INIA – Fundación Conde del Valle de Salazar. Madrid, España.
Mutke S, Gordo J, Climent J, Gil L, 2003. Shoot Growth and Phenology Modelling of Grafted Stone Pine (Pinus pinea) in Inner Spain. Ann For Sci 60(6): 527-537. http://dx.doi.org/10.1051/forest:2003046
Mutke S, Gordo J, Gil L, 2005a. Variability of Mediterranean stone pine cone production: yield loss as response to climatic change. Agric For Met 132: 263–272. http://dx.doi.org/10.1016/j.agrformet.2005.08.002
Mutke S, Sievänen R, Nikinmaa E, Perttunen J, Gil L, 2005b. Crown architecture of grafted stone pine (Pinus pinea L.): shoot growth and bud differentiation. Trees-Structure and Function 19(1): 15-25. http://dx.doi.org/10.1007/s00468-004-0346-7
Mutke S, Calama R, González-Martinez S, Montero G, Gordo J, Bono D, Gil L, 2012. Mediterranean Stone Pine: Botany and Horticulture. Horticultural Reviews 39: 153-202.
Muttke S, 2013. Stone pine in Mediterranean forests. in: Besacier C, Briens M, Duclercq M, Garavaglia V (coord.) State of Mediterranean Forests 2013 (SoMF 2013), FAO Silva Mediterranea, Rome, pp. 83-87.
Mutke S, Martínez J, Gordo J, Nicolas JL, Herrero N, Pastor A, Calama R, 2014. Severe seed yield loss in Mediterranean stone pine cones. medPINE5, 5th International Conference on Mediterranean Pines. Solsona, Spain.
Pardos M, Calama R, Maroschek M, Rammer W, Lexer MJ, 2015. A model-based analysis of climate change vulnerability of Pinus pinea stands under multi-objective management in the Northern Plateau of Spain. Annals of Forest Science 72(8): 1009–1021. http://dx.doi.org/10.1007/s13595-015-0520-7
Prada MA, Gordo FJ, de Miguel J, Mutke S, Catalán-Bachiller G, Iglesias S, Gil L, 1997. Regiones de procedencia de Pinus pinea. Ministerio de Medio Ambiente, Madrid, Spain.
Sala A, Hopping K, McIntire EJ, Delzon S, Crone E, 2012. Masting in whitebark pine (Pinus albicaulis) depletes stored nutrients. New Phytol 196: 189-199. http://dx.doi.org/10.1111/j.1469-8137.2012.04257.x
Sánchez González M, Calama R, Cañellas I, Montero G, 2007. Variables influencing cork thickness in Spanish cork oak forests: a modelling approach. Ann For Sci 67(2): 301-312. http://dx.doi.org/10.1051/forest:2007007
Sork VL, 1993. Evolutionary ecology of mast seeding in temperate and tropical oaks (Quercus spp). Vegetatio 107/108: 133–147. http://dx.doi.org/10.1007/978-94-011-1749-4_9
Strong W, 2015. Lodgepole pine seedset increase by mesh bagging is due to exclusion of Leptoglossus occidentalis (Hemiptera: Coreidae). J Entomol Soc Brit Columbia 112: 3-17.
Spathelf P, van der Maaten E, van der Maaten-Theunissen M, Campioli M, Dobrowolska D, 2014. Climate change impacts in European forests: the expert views of local observers. Ann For Sci 71: 131–137. http://dx.doi.org/10.1007/s13595-013-0280-1
Tu W, 2002. Zero-inflated data. In: El-Shaarawi AH, Piegorsch WW (Eds.) Encyclopedia of Environmetrics. John Wiley and Sons, Chichester, United Kingdom, pp. 2387–2391.
Vázquez-Piqué J, Pereira H, 2008. What to take into account to develop cork weight models?: review and statistical considerations. Forest Systems 17(3): 199-215.
http://dx.doi.org/10.5424/srf/2008173-01035
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