Shifts in tree allometry in a tropical dry forest: implications for above-ground biomass estimation

  • Gustavo Ramírez-Ramírez Universidad Autónoma de Yucatán
  • Luis Ramírez y Avilés Universidad Autónoma de Yucatán
  • Francisco Javier Solorio-Sánchez Universidad Autónoma de Yucatán
  • Jorge Augusto Navarro-Alberto Universidad Autónoma de Yucatán
  • Juan Manuel Dupuy-Rada Centro de Investigación Científica de Yucatán
keywords: Allometric equations, growth, resource allocation, segmented regression, tree-size categories


Background: Accurate estimations of aboveground biomass (AGB) based on allometric models are needed to implement climate-change mitigation strategies. However, allometry can change with tree size.

Questions: Does allometry in a tropical dry forest change with tree size? Does combining different allometric equations provide better AGB estimates than using a single equation?

Study site and dates: San Agustín Ejido, Yucatán, México, 2016.

Methods: Forty-seven trees of 18 species with 2.5 to 41.5 cm in diameter at breast height (DBH) were sampled. Stems and branches were sectioned, and samples were dried and weighed to estimate tree AGB. Segmented linear regression was used to evaluate changes in allometry between DBH, height and AGB. Different equations were tested for each size category identified, and the best models and model-combinations selected.

Results: A shift in the AGB-height relationship was found, defining two tree-size categories (2.5-9.9 cm and ? 10 cm in DBH), with the inflection point corresponding to the average canopy height (12.2 m). The best models were AGB = exp(-2.769+0.937ln(D2HPw)) for trees < 10 cm DBH and AGB = exp(-9.171+1.591lnD+3.902lnH+0.496lnPw) for trees ? 10 cm DBH (R2 = 0.85 and R2 = 0.92, respectively). The combination of these models produced more accurate AGB estimates than a single model or combinations involving regional models with larger sample sizes.

Conclusions: These results highlight the importance of locally-developed models and suggest changes in allometry and resource allocation: towards height growth for small trees, thereby reducing the risk of suppression, versus towards AGB growth for larger trees, thereby maximizing stability and resource acquisition.


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Author Biographies

Gustavo Ramírez-Ramírez, Universidad Autónoma de Yucatán
Campus de Ciencias Biológicas y Agropecuarias
Luis Ramírez y Avilés, Universidad Autónoma de Yucatán
Campus de Ciencias Biológicas y Agropecuarias
Francisco Javier Solorio-Sánchez, Universidad Autónoma de Yucatán
Campus de Ciencias Biológicas y Agropecuarias
Jorge Augusto Navarro-Alberto, Universidad Autónoma de Yucatán
Campus de Ciencias Biológicas y Agropecuarias
Juan Manuel Dupuy-Rada, Centro de Investigación Científica de Yucatán
Unidad de Recursos Naturales
Shifts in tree allometry in a tropical dry forest: implications for above-ground biomass estimation


Anderson-Teixeira KJ, Davies SJ, Bennett AC, Gonzalez-Akre EB, Muller-Landau HC, Wright JS, …Zimmerman J. 2015. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Global Change Biology 21: 528–549. DOI:10.1111/gcb.12712

Angelsen A, Brockhaus M, Sunderlin WD, Verchot LV. 2012. Analysing REDD+: Challenges and choices. Jakarta: Center for International Forestry Research, CIFOR.

Baccini AGSJ, Goetz SJ, Walker WS, Laporte NT, Sun M, Sulla-Menashe D,... Houghton RA. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2(3): 182-185. DOI: 10.1038/NCLIMATE1354

Barrera-Bassols N, Toledo VM. 2005. Ethnoecology of the Yucatec Maya: symbolism, knowledge and management of natural resources. Journal of Latin American Geography 4(1): 9–41.

Bautista F, Frausto O, Ihl T, Aguilar Y. 2015. Actualización del mapa de suelos de Yucatán, México: enfoque geomorfopedológico y WRB. Ecosistemas y Recursos Agropecuarios 2(6): 303-315.

Bongers F, Chazdon R, Poorter L, Peña-Claros M. 2015. The potential of secondary forests. Science 348(6235): 642-643. DOI: 10.1126/science.348.6235.642-c

Brown S. 1997. Estimating biomass and biomass change of tropical forests. Forest Resources Assessment Publication. Forestry Papers, 134, 55.

Caballero F. 2011. Selección de modelos mediante criterios de información en análisis factorial. Unpubl. Ph.D Thesis, Universidad de Granada.

Chaplin-Kramer R, Ramler I, Sharp R, Haddad NM, Gerber JS, West PC, ... King H. 2015. Degradation in carbon stocks near tropical forest edges. Nature Communications 6.

Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. 2004. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London B: Biological Sciences 359(1443): 409-420. DOI 10.1098/rstb.2003.1425

Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q. et al. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1): 87-99. DOI 10.1007/s00442-005-0100-x

Chave J, Muller-Landau H, Baker TR, Easdale TA, ter Steege H, Webb CO. 2006. Regional and phylogenetic variation of wood density across 2456 Neotropical tree species. Ecological Applications 16(6): 2356-2367.[2356:RAPVOW]2.0.CO;2

Chave J, Réjou?Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WB, ... Vieilledent G. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 20(10): 3177-3190. doi: 10.1111/gcb.12629

Chazdon RL, Broadbent EN, Rozendaal DMA, Bongers F, Zambrano AMA, Aide TM… Poorter L. 2016. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Science Advances 2:e1501639 doi:10.1126/sciadv.1501639.

Chen Q, Laurin GV, Valentini R. 2015. Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels. Remote Sensing of Environment 160: 134-143.

Comisión Nacional Forestal (CONAFOR). 2013. Inventario Nacional y de Suelos. Manual y procedimientos para el muestreo de campo. Zapopan, Jalisco: CONAFOR.

Dixon R, Brown S, Houghton REA, Solomon AM, Trexler MC, Wisniewski J. 1994. Carbon pools and flux of global forest ecosystems. Science 263(5144): 185-189. DOI: 10.1126/science.263.5144.185

Dupuy JM, Hernández?Stefanoni JL, Hernández?Juárez RA, Tetetla?Rangel E, López?Martínez JO, Leyequién?Abarca E, ...May?Pat F. 2012. Patterns and correlates of tropical dry forest structure and composition in a highly replicated chronosequence in Yucatan, Mexico. Biotropica 44(2): 151-162. DOI: 10.1111/j.1744-7429.2011.00783.x

FAO Global Forest Resources Assessment. 2010. FAO Forestry Paper 163. Food and Agriculture Organization of the United Nations.

Feldpausch TR, Lloyd J, Lewis SL, Brienen RJ, Gloor M, Monteagudo Mendoza A, ... Phillips OL. 2012. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9: 3381-3403. doi:10.5194/bg-9-3381-2012.

Flores Guido JS, Espejel Carvajal I. 1994. Tipos de vegetación de la Península de Yucatán. Etnoflora Yucatanense. Fascículo 3. Mérida: Universidad Autónoma de Yucatán.

Fonseca W, Alice F, Rey JM. 2009. Modelos para estimar la biomasa de especies nativas en plantaciones y bosques secundarios en la zona Caribe de Costa Rica. Bosque 30(1): 36-47.

García E. 1973. Modificaciones al sistema de clasificación climática de Köppen para adaptarlo a las condiciones de la República Mexicana. 2nd Ed. Mexico: Universidad Nacional Autónoma de México.

Gayon J. 2000. History of the Concept of Allometry 1. American Zoologist 40(5): 748-758.

Hernandez-Stefanoni JL, Dupuy JM. 2008. Effects of landscape patterns on species density and abundance of trees in a tropical subdeciduous forest of the Yucatan Peninsula. Forest Ecology and Management 255: 3797–3805. doi:10.1016/j.foreco.2008.03.019

Hernández-Stefanoni JL, Dupuy JM, Johnson KD, Birdsey R, Tun-Dzul F, Peduzzi A, ... López-Merlín D. 2014. Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR. Remote Sensing 6(6): 4741-4763. doi:10.3390/rs6064741

Hui D, Deng Q, Tian H, Luo Y. 2017. Climate Change and Carbon Sequestration in Forest Ecosystems. Handbook of Climate Change Mitigation and Adaptation, 555-594.

Jucker T, Caspersen J, Chave J, Antin C, Barbier N, Bongers F, ... Coomes DA. 2017. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology 23(1): 177-190. doi: 10.1111/gcb.13388

Klepper O, Rouse DI. 1991. A procedure to reduce parameter uncertainty for complex models by comparison with real system output illustrated on a potato growth model. Agricultural Systems 36: 375-395.

Le Quéré C, Moriarty R, Andrew RM, Peters GP, Ciais P, Friedlingstein P, ... Zeng N. 2015. Global carbon budget 2014. Earth System Science Data 7(1): 47-85. doi:10.5194/essdd-6-689-2013

Loague K, Green RE. 1991. Statistical and graphical methods for evaluating solute transport models: Over-view and applications. Journal of Contaminant Hydrology 7: 51-73.

López-Martínez JO, Sanaphre-Villanueva L, Dupuy JM, Hernández-Stefanoni JL, Meave JA, Gallardo-Cruz JA. 2013. ?-Diversity of functional groups of woody plants in a tropical dry forest in Yucatan. PLOS ONE 8(9): e73660. doi:10.1371/journal.pone.0073660

Malhi Y, Wood D, Baker TR, Wright J, Phillips OL, Cochrane T, ... Vicenti B. 2006. The regional variation of aboveground live biomass in old?growth Amazonian forests. Global Change Biology 12(7): 1107-1138. doi: 10.1111/j.1365-2486.2006.01120.x

Medina-Peralta S, Vargas-Villamil L, Navarro-Alberto J, Canul-Pech C, Peraza-Romero S. 2010. Comparación de medidas de desviación para validar modelos sin sesgo, sesgo constante o proporcional. Universidad y Ciencia 26(3): 255-263.

Mermoz S, Réjou-Méchain M, Villard L, Le Toan T, Rossi V, Gourlet-Fleury S. 2015. Decrease of L-band SAR backscatter with biomass of dense forests. Remote Sensing of Environment 159: 307-317.

Midgley JJ. 2003. Is bigger better in plants? The hydraulic costs of increasing size in trees. Trends in Ecology and Evolution 18: 5–6.

Molto Q, Hérault B, Boreux JJ, Daullet M, Rousteau A, Rossi V. 2013. Predicting tree heights for biomass estimates in tropical forests. Biogeosciences Discussions 10(5): 8611-8635. doi:10.5194/bgd-10-8611-2013

Muggeo VM. 2008. Segmented: an R package to fit regression models with broken-line relationships. R News 8(1): 20-25.

Návar-Cháidez J, Rodríguez-Flores FDJ, Domínguez-Calleros PA. 2013. Ecuaciones alométricas para árboles tropicales: aplicación al inventario forestal de Sinaloa, México. Agronomía Mesoamericana 24(2): 347-356.

Niklas KJ. 1995. Size-dependent allometry of tree height, diameter and trunk-taper. Annals of Botany 75: 217–227.

Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, ... Werner AK. 2011. A large and persistent carbon sink in the world’s forests. Science 333(6045): 988-993. DOI: 10.1126/science.1201609

Picard N, Saint-Andre L, Henry M. 2012. Manual de construcción de ecuaciones alométricas para estimar el volumen y la biomasa de los árboles: del trabajo de campo a la predicción. Rome: FAO, Centre de Coopération Internationale en Recherche Agronomique pour le Développement.

Poorter L, Bongers F, Aide TM, Zambrano AMA, Balvanera P, Becknell JM, ... Rozendaal DMA. 2016. Biomass resilience of Neotropical secondary forests. Nature 530(7589): 211-214.

R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL.

Ramírez Ramírez G, Dupuy Rada JM, Ramírez-Avilés L, Solorio Sánchez FJ. 2017. Evaluación de ecuaciones alométricas de biomasa aérea enselvas secas de Yucatán, México. Madera y Bosques 23(2): 163:179. doi:10.21829/myb.2017.2321452

Sanaphre-Villanueva L, Dupuy JM, Andrade J, Reyes-García C, Paz H, Jackson PC. 2016. Functional diversity of small and large tres along secondary succession in a tropical dry forest. Forests 7: 163. DOI:10.3390/f7080163.

Segura M, Andrade Castañeda HJ. 2008. ¿Cómo construir modelos alométricos de volumen, biomasa o carbono de especies leñosas perennes? Agroforestería en las Américas (CATIE) 46: 89-96.

Stas SM, Rutishauser E, Chave J, Anten N, Laumonier Y. 2017. Estimating the aboveground biomass in an old secondary forest on limestone in the Moluccas, Indonesia: Comparing locally developed versus existing allometric models. Forest Ecology and Management 389: 27-34.

Tedeschi LO. 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89: 225-247. doi:10.1016/j.agsy.2005.11.004

Timothy D, Onisimo M, Riyad I. 2016. Quantifying aboveground biomass in African environments: A review of the trade-offs between sensor estimation accuracy and costs. Tropical Ecology 57(3): 393-405.

United Nations Framework Convention on Climate Change. 2008. Report of the Conference of the Parties on its thirteenth session, held in Bali from 3 to 15 December 2007. Addendum, Part 2. Document FCCC/CP/2007/6/Add.1. UNFCCC, Bonn, Germany.

Urquiza-Haas T, Dolman PM, Peres CA. 2007. Regional scale variation in forest structure and biomass in the Yucatan Peninsula, Mexico: effects of forest disturbance. Forest Ecology and Management 247(1): 80-90. doi:10.1016/j.foreco.2007.04.015

Van Breugel M, Ransijn J, Craven D, Bongers F, Hall JS. 2011. Estimating carbon stock in secondary forests: decisions and uncertainties associated with allometric biomass models. Forest Ecology and Management 262(8): 1648-1657. doi:10.1016/j.foreco.2011.07.018

Weiner J. 2004. Allocation, plasticity and allometry in plants. Perspectives in Plant Ecology, Evolution and Systematics 6(4): 207-215.

Yang J, Greenwood DJ, Rowell DL, Wadsworth GA, Burns IG. 2000. Statistical methods for evaluating a crop nitrogen simulation model, N_ABLE. Agricultural Systems 64(1): 37-53.

How to Cite
Ramírez-Ramírez, G., Ramírez y Avilés, L., Solorio-Sánchez, F. J., Navarro-Alberto, J. A., & Dupuy-Rada, J. M. (2019). Shifts in tree allometry in a tropical dry forest: implications for above-ground biomass estimation. Botanical Sciences, 97(2), 167-179.