Blog List

Thursday, 27 October 2016

Importance of tree basic density in biomass estimation and associated uncertainties: a case of three mangrove species in Tanzania

Published Date
Original Paper
First Online: 
13 October 2016
DOI: 10.1007/s13595-016-0583-0

Cite this article as: 
Njana, M.A., Meilby, H., Eid, T. et al.
 Annals of Forest Science (2016). doi:10.1007/s13595-016-0583-0

Author
  • Marco Andrew NjanaEmail author
  • Henrik Meilby
  • Tron Eid
  • Eliakimu Zahabu
  • Rogers Ernest Malimbwi

    • Abstract

    Key message

    Aboveground and belowground tree basic densities varied between and within the three mangrove species. If appropriately determined and applied, basic density may be useful in estimation of tree biomass. Predictive accuracy of the common (i.e. multi-species) models including aboveground/belowground basic density was better than for common models developed without either basic density. However, species-specific models developed without basic density performed better than common models including basic density.

    Context

    Reducing Emissions from Deforestation and forest degradation and the role of sustainable forest management, conservation and enhancement of carbon stocks (REDD+) initiatives offer an opportunity for sustainable management of forests including mangroves. In carbon accounting for REDD+, it is required that carbon estimates prepared for monitoring reporting and verification schemes should ensure that all known sources of uncertainty are minimised as much as possible. However, uncertainties of applying indirect method of biomass determination are poorly understood.

    Aims

    This study aimed to assess importance of tree basic density in modelling aboveground and belowground biomass and examine uncertainties in estimation of tree biomass using indirect methods.

    Methods

    This study focused on three dominant mangrove species (Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith and Rhizophora mucronata Lam.) in Tanzania. A total of 120 trees were destructively sampled for aboveground biomass, and 30 among them were sampled for belowground biomass. Tree merchantable volume and both aboveground and belowground basic densities were determined. Biomass models including basic density as a predictor variable were developed using the non-linear mixed-effects modelling approach.

    Results

    Results showed that both tree aboveground and belowground basic density varied significantly between sites between tree species, among individuals of the same species and between tree components. The use of tree- and component-specific aboveground basic density resulted in unbiased tree aboveground biomass estimates; however, uncertainties were high when using aboveground basic density values from the Global Wood Density (GWD) database. Predictive accuracy of the common models including aboveground/belowground basic density was better than for the common models developed previously without basic density. However, the species-specific models developed previously without basic density were superior to the common models including basic density developed in the present study.

    Conclusion

    Tree aboveground and belowground basic densities were useful in modelling tree aboveground and belowground biomass, respectively. This is demonstrated by improved goodness of fit associated with inclusion of basic density. However, species-specific models developed without basic density performed better than common models including basic density. If appropriately determined and applied, basic density may be useful in estimation of tree biomass and hence contribute to improved accuracy of carbon stock estimates for REDD+ and sustainable management of mangroves in general.

    Keywords

    Tree aboveground and belowground biomassInter- and intra-tree basic density variationBiomass modelsIndirect tree biomass estimationMixed-effects models

    References
    1. Aksornkoae S, Maxwell GS, Havanond S, Panichsuko S (1992) Plants in mangroves. IUCN Asian Regional Office, BangkokGoogle Scholar
    2. Alongi DM (2011) Carbon payments for mangrove conservation: ecosystem constraints and uncertainties of sequestration potential. Environ Sci Pol 14:462–470CrossRefGoogle Scholar
    3. Angelsen A, Hofstad O (2008) Inputs to the development of a National Reducing carbon Emissions from Deforestation and forest Degradation (REDD) strategy in Tanzania, Norwegian University of Life Sciences (UMB). Report for the Norwegian Embassy in Tanzania
    4. Auty D, Achim A, Macdonald E, Cameroon AD, Gardiner BA (2014) Models for predicting wood density variation in Scots pine. Forestry 87:449–458CrossRefGoogle Scholar
    5. Bretz F, Hothorn T, Westfall P (2011) Multiple comparisons using R. CRC Press, New YorkGoogle Scholar
    6. Brown S (1997) Estimating biomass change of tropical forests: Primer, FAO Forestry Paper 134, Rome, Italy
    7. Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R (2004) Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc London B 359:409–420CrossRefGoogle Scholar
    8. Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson B, Ogawa H, Puig H, Riéra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99CrossRefPubMedGoogle Scholar
    9. Chave J, Coomes DA, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecological Lett 12(4):351–366CrossRefGoogle Scholar
    10. Clark DC, Kellner JR (2012) Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci 23:1191–1196CrossRefGoogle Scholar
    11. Comley BWT, McGuinness KA (2005) Above- and belowground biomass, and allometry of four common northern Australian mangroves. Aust J Bot 53:431–436CrossRefGoogle Scholar
    12. Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen M (2011) Mangroves among the most carbon-rich forests in the tropics. Nat Geosci 4:293–297CrossRefGoogle Scholar
    13. Duncan C, Primavera JH, Pettorelli N, Thompson JR, Loma RJA, Koldewey HJ (2016) Rehabilitating mangrove ecosystem services: A case study on the relative benefits of abandoned pond reversion from Panay Island, Philippines. Marine Pollution Bulletin 109: 772-782
    14. FAO (2010) Global Forest Resource Assessment 2010. FAO Forestry Paper 163. Rome, Italy
    15. Fatoyinbo TE, Simard M, Washington-Allen RA, Shugart H (2008) Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove forests with Landsat ETM+ and shuttle radar topography mission elevation data. Geophys Res 113:1–14CrossRefGoogle Scholar
    16. Fearnside PM (1997) Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecol Manag 90:59–87CrossRefGoogle Scholar
    17. Gałecki A, Burzykowski T (2013) Linear mixed-effects models using R: a step by step approach. Springer, New YorkGoogle Scholar
    18. Githiomi JK, Kariuki JG (2010) Wood basic density of Eucalyptus grandis from plantations in central rift valley, Kenya: variation with age, height level and between sapwood and heartwood. J Trop For Sci 22:281–286Google Scholar
    19. Grassi G, Monni S, Federici S, Achard F, Mollicone D (2008) Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates. Environ Res Lett 3:1–11CrossRefGoogle Scholar
    20. Henry M, Besnard A, Asante WA, Eshun J, Adu-Bredu S, Valentini R (2010) Wood density, phytomass variations within and among trees and allometric equations in a tropical rainforest of Africa. Forest Ecol Manag 260:1375–1388CrossRefGoogle Scholar
    21. IPCC (2003) IPCC good practice guidance for LULUCF. Institute for Global Environmental Strategies (IGES) for the IPCC. Kanagawa, Japan
    22. IPCC (2006) Guidelines for National Greenhouse Gas Inventories. IGES, JapanGoogle Scholar
    23. Jachowski NRA, Quak MSY, Friess DA, Duangnamon D, Webb EL, Ziegler AD (2013) Mangrove biomass estimation in South-west Thailand using machine learning. Appl Geogr 45:311–321CrossRefGoogle Scholar
    24. Ketterings QM, Coe R, van Noordwijk M, Ambagau Y, Palm CA (2001) Reducing uncertainty in the use allometric biomass equations for predicting aboveground tree biomass in mixed secondary forests. Forest Ecol Manag 146:199–209CrossRefGoogle Scholar
    25. Komiyama A, Poungparn S, Kato S (2005) Common allometric equations for estimating the tree weight of mangroves. J Trop Ecol 21:471–477CrossRefGoogle Scholar
    26. Komiyama A, Ong JE, Poungparn S (2008) Allometry, biomass, and productivity of mangrove forests: a review. Aquat Bot 89:128–137CrossRefGoogle Scholar
    27. Kristensen E, Bouillon S, Dittmar T, Marchand C (2008) Organic carbon dynamics in mangrove ecosystems: a review. Aquat Bot 89:201–219CrossRefGoogle Scholar
    28. Langner A, Miettinen J, Siegert F (2007) Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Glob Change Biol 13:2329–2340CrossRefGoogle Scholar
    29. Locatelli T, Binet T, Kairo JG, King L, Madden S, Patenaude G, Upton C, Huxham M (2014) Turning the tide: how blue carbon and payments for ecosystem services (PES) might help save mangrove forests. Ambio 43:981–995CrossRefPubMedPubMedCentralGoogle Scholar
    30. Luoga EJ, Malimbwi RE, Kajembe GC, Zahabu E, Shemwetta DTK, Lyimo-Macha J, Mtakwa P, Mwaipopo CS (2004) Tree species composition and structures of Jasini Mkwajuni mangrove forest at Pangani, Tanzania. J Tanzan Assoc For 10:42–47Google Scholar
    31. MNRT (Ministry of Natural Resources and Tourism) (2015) NAFORMA (National Forest Monitoring and Assessments of Tanzania) main results. Dar es Salaam
    32. Mwakalukwa EE, Meilby H, Treue T (2014) Volume and aboveground biomass models for dry miombo woodlands in Tanzania. Intern J For Res doi. doi:10.1155/2014/531256Google Scholar
    33. Nagelkerken I, Blaber SJM, Bouillon S, Green P, Haywood M, Kirton LG, Meynecke JO, Pawlik J, Penrose HM, Sasekumar A (2008) The habitat function of mangroves for terrestrial and marine fauna: a review. Aquat Bot 89:155–185CrossRefGoogle Scholar
    34. Njana MA, Eid T, Zahabu E, Malimbwi R (2015) Procedures for quantification of belowground biomass of three mangrove tree species. Wetlands Ecol and Manage 23:749–764CrossRefGoogle Scholar
    35. Njana MA, Bollandsås OM, Eid T, Zahabu E, Malimbwi RE (2016) Above- and belowground tree biomass models for three mangrove species in Tanzania: a non-linear mixed-effects modelling approach. Ann For Sci 73:353–369CrossRefGoogle Scholar
    36. Nshare JS, Chitiki A, Malimbwi RE, Kinana BM, Zahabu E (2007) The current status of the mangrove forest along seashore at Salenda bridge, Dar es Salaam, Tanzania. J Tanzan Assoc For 11:172–179Google Scholar
    37. Ong JE, Gong WK, Wong CH (2004) Allometry and partitioning of the mangrove, Rhizophora apiculata. Forest Ecol Manag 88:395–408CrossRefGoogle Scholar
    38. Picard N, Saint-André L, Henry M (2012) Manual for building tree volume and biomass allometric equations: from field measurement to prediction. Food and Agricultural Organization of the United Nations, Rome, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier
    39. Pinheiro J, Bates D (2000) Mixed effects models in S and S-plus. Springer, New YorkCrossRefGoogle Scholar
    40. Purnobasuki H (2013) Characteristics of root caps in four root types of Avicennia marina(Forsk.) Vierh. Am J P Sci 4:853–858CrossRefGoogle Scholar
    41. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/. Accessed 20 Nov 2013
    42. Saenger P, Hegerl EJ, Davie JDS (eds) (1983) Global status of mangrove ecosystems by the Working Group on Mangrove Ecosystems of the IUCN Commission on Ecology in cooperation with the United Nations Environment Programme and the World Wildlife Fund. Environmentalist 3:1–88
    43. Saintini NS, Schmitz N, Lovelock CE (2012) Variation in wood density and anatomy in a widespread mangrove species. Trees 26:1–9CrossRefGoogle Scholar
    44. Somogyi Z, Cienciala E, Mäkipää R, Muukkonen P, Lehtonen A, Weiss P (2007) Indirect methods of large-scale forest biomass estimation. Eur J Forest Res 126:197–207CrossRefGoogle Scholar
    45. Spalding M, Kainuma M, Collings L (2010) World atlas of mangroves. A collaborative project of ITTO, ISME, FAO, UNEP-WCMC, UNESCO-MAB, UNU-INWEH and TNC. Earthscan, LondonGoogle Scholar
    46. Tamooh F, Huxham M, Karachi M, Mencuccini M, Kairo JG, Kirui B (2008) Below-ground root yield and distribution in natural and replanted mangrove forests at Gazi bay, Kenya. Forest Ecol Manag 256:1290–1297CrossRefGoogle Scholar
    47. Tomlinson PB (1986) The botany of mangroves. Cambridge University Press, CambridgeGoogle Scholar
    48. UNFCCC (2011) United Nations Framework Convention on Climate Change (2011) Outcome of the Ad Hoc Working Group on Long-term Cooperative Action Under the Convention (Draft Decision [−/CP.17])
    49. Valiela I, Bowen JL, York JK (2001) Mangrove forests: one of the world’s threatened major tropical environments. Bioscience 51:807–815CrossRefGoogle Scholar
    50. Walther BA, Moore JL (2005) The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28:815–829CrossRefGoogle Scholar
    51. West PW (2009) Tree and forest measurements. 2nd edition. Springer-Verlag, Berlin HeidelbergCrossRefGoogle Scholar
    52. Wiemann MC, Williamson GB (2012) Density and specific gravity metrics in biomass research. USDA Forest Service, Forest Products Laboratory, General Technical Report, FPL-GTR-208
    53. Williamson GB, Wiemann MC (2010) Measuring wood specific gravity correctly. Am J Bot 97:519–524CrossRefPubMedGoogle Scholar
    54. Wylie L, Sutton-Grier AE, Moore A (2016) Keys to successful blue carbon projects: lessons learned from global case studies. Mar Policy 65:76–84CrossRefGoogle Scholar
    55. Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J (2009) Data from: towards a worldwide wood economics spectrum. Dryad Digital Repository. doi:10.5061/dryad.234Google Scholar
    56. Zhang K, Liu H, Xu H, Shen J, Rhome J, Smith TJ (2012a) The role of mangroves in attenuating storm surges. Estuar Coast Shelf S 103:11–23CrossRefGoogle Scholar
    57. Zhang L, Deng X, Lei X, Xiang W, Peng C, Lei P, Yan W (2012b) Determining stem biomass of Pinus massoniana L. through variations in basic density. Forestry 85:601–609CrossRefGoogle Scholar
    58. Zhou X, Brandle JR, Schoeneberger MM, Awada T (2007) Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species: shelterbelt-grown Russian-olive. Ecol Model 20:311–323CrossRefGoogle Scholar

    For further details log on website :
    http://link.springer.com/article/10.1007/s13595-016-0585-y
    at

    No comments:

    Post a Comment

    Subscribe to: Post Comments (Atom)

    Advantages and Disadvantages of Fasting for Runners

    Author BY   ANDREA CESPEDES  Food is fuel, especially for serious runners who need a lot of energy. It may seem counterintuiti...