Understanding bark thickness variations for Araucaria angustifolia in southern Brazil

Abstract

This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height (D_BH), height (H), crown base height (C_BH), crown length (C_L), social position (S_P), stoniness (S_T), position on the relief (P_R), vitality (V_T) and branch arrangement (B_A) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to D_BH, with 0.76 as coefficient of determination (R ²), 0.540 as Mean Absolute Error (M_AE) and 22.4 root-mean-square error in percentage (R_MSE%); (2) the trend changed according to bark colour, with significant differences for the intersection (

{\beta }_0

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– Pr > F: p = 0.0124) and slope (

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– Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: D_BH (ρ = 0.88), H (ρ = 0.58), C_BH (ρ = 0.46), S_P (ρ = − 0.52), and B_A (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R ² = 0.99) and accuracy (R_MSE% = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species.

Keywords

Dendrometry attributes / Crown characteristics / Prediction models / Bark factor / Parana-pine

References

[1] Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G. Köppen’s climate classification map for Brazil. Meteorol Z, 2013, 2: 711-728.

[2] Barbosa LO (2018) Efeito da competição no incremento em área transversal de Araucaria angustifolia (Bertol) Kuntze em Floresta Ombrófila Mista no sul do Brasil. Dissertation. Santa Maria: Universidade Federal de Santa Maria, p. 91

[3] Binoti MLMS, Binoti DHB, Leite HG. Aplicação de redes neurais artificiais para estimação da altura de povoamentos equiâneos de eucalipto. Árvore, 2013, 37(4): 639-645.

[4] Binoti MLMS, Binoti DHB, Leite HG, Garcia SLR, Ferreira MZ, Rode R, Silva AAL. Redes neurais artificiais para estimação do volume de árvores. Árvore, 2014, 38(2): 283-288.

[5] Binoti MLMS, Binoti DHB, Leite HG, Silva AAL, Pontes C. Utilização de redes neurais artificiais para a projeção da distribuição diamétrica de povoamento equiâneos. Árvore, 2014, 38(4): 747-754.

[6] Carvalho PER. Espécies florestais brasileiras: recomendações silviculturais, potencialidades e usos da madeira, 1994, Colombo: EMBRAPA-CNPF 640

[7] Carvalho PER. Espécies florestais brasileiras, 2003, Brasília: EMBRAPA-CNPF, Colombo/EMBRAPA-SPI 1039

[8] Castaño-Santamaría J, Crecente-Campo F, Fernández-Martinez JL, Barrio-Anta M, Obeso JR. Tree height prediction approaches for uneven-aged beech forests in northwestern Spain. For Ecol Manag, 2013, 307: 63-73.

[9] Castro RVO, Soares CPB, Martins FB, Leite HG. Crescimento e produção de plantios comerciais de eucalipto estimados por duas categorias de modelos. Pesq Agropec Bras, 2013, 48(3): 287-295.

[10] Castro RVO, Soares CPB, Leite HG, De Souza AL, Nogueira GS, Martins FB. Individual growth model for Eucalyptus stands in Brazil using Artificial Neural Network. ISRN Forestry, 2013, 2013: 1-12.

[11] Ceccatto GNO. Pinho Brasileiro, 1943, Rio de Janeiro: Ministério da Agricultura, Serviço de Informação Agrícola 39

[12] Costa EA.(2011). Influência de variáveis dendrométricas e morfométricas da copa no incremento periódico de Araucaria angustifolia (Bertol.) Kuntze, Lages, SC. Dissertation. Santa Maria: Universidade Federal de Santa Maria, p 140

[13] Costa EA, Finger CAG. Efeito da Competição nas Relações Dimensionais de Araucária. Floresta e Ambiente, 2017, 24: e20150145.

[14] Diamantopoulou MJ. Artificial neural networks as an alternative tool in pine bark volume estimation. Comput Electron Agric, 2005, 48: 235-244.

[15] Diamantopoulou MJ. Tree-bole volume estimation on standing pine trees using cascade correlation artificial neural network models. CIGR J, 2006, 8: 1-14.

[16] Goodfellow I, Bengio Y, Courville A. Deep Learning, 2016, Cambridge: The MIT Press 800

[17] Gorgens EB, Leite HG, Gleriani JM, Soares CPB, Ceolin A. Estimação do volume de árvores utilizando redes neurais artificiais. Árvore, 2009, 33(6): 1141-1147.

[18] Haykin S. Redes neurais: princípios e prática, 2007 2 Porto Alegre: Bookman 900

[19] Hueck K (1972) As florestas da América do Sul. Ed. da UnB, Brasília/Polígono, São Paulo, p. 466

[20] IUCN (2006) IUCN red list of threatened species. http://www.iucnredlist.org. Accessed 5 Dec 2006

[21] Johnson TS, Wood GB. Simple linear model reliably predicts bark thickness of Radiata pine in the Australian Capital Territory. For Ecol Manag, 1987, 22: 173-183.

[22] Kershaw JA, Ducey MJ, Beers TW, Husch B. Forest Mensuration, 2017 5 Hoboken: Wiley 630

[23] Kozak A, Yang RC. Equations for estimating bark volume and thickness of commercial trees in British Columbia. Forest Chron, 1981, 57(3): 112-115.

[24] Kozlowski TT, Pallardy SG. Physiology of woody plants, 1996 2 San Diego: Academic Press 411

[25] Laar A. Bark thickness and bark volume of Pinus patula in South Africa. South Hemisphere Forest J, 2007, 69(3): 165-168.

[26] Laasasenaho J, Melkas T, Aldén S. Modelling bark thickness of Picea abies with taper curves. For Ecol Manag, 2005, 206: 35-47.

[27] Lawes MJ, Richards A, Dathe J, Midgley J. Bark thickness determines fire resistance of selected tree species from fire-prone tropical savanna in north Australia. Plant Ecol, 2011, 212(12): 2057-2069.

[28] Leal FA, Miguel EP, Matricardi EAT, Pereira RS. Redes neurais artificiais na estimativa de volume em um plantio de eucalipto em função de fotografias hemisféricas e número de árvores. Rev Bras Biometr, 2015, 33(2): 233-249.

[29] Leite HG, Silva MLM, Binoti DHB, Fardin L, Takiza WFH. Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks. Eur J Forest Res, 2011, 130(2): 263-269.

[30] Li R, Weiskittel AR. Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed effects modeling approach: comparison of model forms and subsampling strategies. Eur J Forest Res, 2011, 130(2): 219-233.

[31] Marchiori JNC. Dendrologia das gimnospermas, 2005 2 Santa Maria: UFSM 161

[32] Milliken GA, Johnson FE. Analysis of messy data volume III: analysis of covariance, 2002, Boca Raton: Chapman e Hall.

[33] Molina JGA, Hadad MA, Domínguez DP, Roig FA. Tree age and bark thickness as traits linked to frost ring probability on Araucaria araucana trees in northern Patagonia. Dendrochronologia, 2016, 37: 116-125.

[34] Muhairwe CK. Bark thickness equations for five commercial tree species in regrowth forests of Northern New South Wales. Aust Forest, 2000, 63(1): 34-43.

[35] Paine CET, Stahl C, Courtois EA, Patiño S, Sarmiento C, Baraloto C. Functional explanations for variation in bark thickness in tropical rain forest trees. Funct Ecol, 2010, 24: 1202-1210.

[36] Pausas JG. Bark thickness and fire regime. Funct Ecol, 2015, 29: 315-327.

[37] Pellegrini AFA, Anderegg WRL, Paine T, Hoffmann WA, Kartzinel T, Rabin SS, Sheil D, Franco AC, Pacala SW. Convergence of bark investment according to fire and climate structures ecosystem vulnerability to future change. Ecol Lett, 2017, 20: 307-316.

[38] Reitz R, Klein RM. Flora ilustrada catarinense Araucariáceas, 1966, Itaja: Herbário Barbosa Rodrigues 63

[39] Sanquetta CR, Piva LRO, Wojciechowski J, Corte APD, Schikowski AB. Volume estimation of Cryptomeria japonica logs in southern Brazil using artificial intelligence models. South For, 2017, 80: 29-36.

[40] Schikowski AB, Corte APD, Sanqueta CR. Estudo da forma do fuste utilizando redes neurais artificiais e funções de afilamento. Pesqui Florest Bras, 2015, 35(82): 119-127.

[41] Schwilk DW, Gaetani MS, Poulos HM. Oak bark allometry and fire survival strategies in the Chihuahuan Desert Sky Islands, Texas, USA. PLoS ONE, 2013 8 11 e79285

[42] Seitz RA. Fujimori T, Whitehead D. Crown development of Araucaria angustifolia in its natural environment during sixty years. Crown and canopy structure in relation to productivity, 1986, Forestry and Forest Products Research Institute: Ibaraki 129 146

[43] Silva JA, Estefanel V, Andrae F. Avaliação da dupla espessura de casca em árvores individuais de pinheiro brasileiro, Araucaria angustifolia (Bert.) O.Ktze, referente ao nível do DAP. Ciência Rural, 1975, 5(1): 17-34.

[44] Silva MLM, Binoti DHB, Gleriani JM, Leite HG. Ajuste do modelo de Schumacher e Hall e aplicação de redes neurais artificiais para estimar volume de árvores de eucalipto. Árvore, 2009, 33(6): 1133-1139.

[45] Silveira AC, Hess AF, Schorr LPB, Krefta SM, Santos DV, Filho MDHV, Atanazio KA, Costa EA, Stepka TF, Borsoi GA. Management of Brazilian pine (Araucaria angustifolia (Bertol) Kuntze) based on the Liocourt model in a mixed Ombrophilous forest in Southern Brazil. Aust J Crop Sci, 2018, 12(02): 311-317.

[46] Soares FA, Flôres EL, Cabacinha CD, Carrijo GA, Veiga ACP. Recursive diameter prediction and volume calculation of Eucalyptus trees using multilayer perceptron networks. Comput Electron Agric, 2011, 78(1): 19-27.

[47] Souza RR (2013) Estudo da forma do fuste de árvores de eucalipto em diferentes espaçamentos. Dissertation. Diamantina: Universidade Federal dos Vales do Jequitinhonha e Mucuri, p. 86

[48] Theodoridis S, Koutroumbas K. Pattern Recognition, 1999 1 San Diego: Academic Press 625

[49] Van Mantgem P, Schwartz M. Bark heat resistance of small trees in Californian mixed conifer forests: testing some model assumptions. For Ecol Manag, 2003, 178(3): 341-352.

[50] Vieira GC, Mendonça AR, Silva GF, Zanetti SS, Silva MM, Santos AR. Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. Sci Total Environ, 2018, 619–620: 1473-1481.

[51] West PW. Tree and forest measurement, 2015 3 Cham: Springer 214

[52] Zeibig-Kichas NE, Ardis CW, Berrill JP, King JP. Bark thickness equations for mixed-conifer forest type in Klamath and Sierra Nevada mountains of California. Int J Forest Res, 2016, 2016: 1-10.