Ecological Indicators (Aug 2021)

Integrating climate, soil and stand structure into allometric models: An approach of site-effects on tree allometry in Atlantic Forest

  • Vinicius Costa Cysneiros,
  • Fernanda Coelho de Souza,
  • Tatiana Dias Gaui,
  • Allan Libanio Pelissari,
  • Gabriel Agostini Orso,
  • Sebastião do Amaral Machado,
  • Daniel Costa de Carvalho,
  • Telmo Borges Silveira-Filho

Journal volume & issue
Vol. 127
p. 107794

Abstract

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Tree allometric models are generally developed at local scales and thus potentially biased when used for different locations and at broader spatial scales. Because allometric relationships vary with forest structure, climatic conditions and edaphic properties, one potential way to address this issue and consistently estimate tree allometry, may involve including new explanatory variables into allometric models. Here, using an extensive dataset of 566 trees widely distributed over Rio de Janeiro state, Brazil, we investigated the influence of stand structure, climate, soil fertility and texture in tree allometry (bark thickness, height, and stem volume) in hyperdiverse and structurally complex Atlantic Forest. Water stress, soil texture and to a lesser extent basal area, soil fertility and precipitation, were strong predictors of tree height and volume. Wetter forests with richer soils support higher-statured trees with greater woody volume, whilst drier environments with moderate to low nutrient availability are associated with small-statured and low tree volume. In contrast, bark thickness was solely determined by soil fertility and ph. Negligible relationship between bark thickness and climatic variables is likely associated with our studied gradient that did not encompass dry forests that are adapted to frequent and intense fires, and where bark investment to stem protection ensures survival. These findings suggest that more appropriate approach to reliably estimate tree height, volume and bark thickness at regional and landscape scale, should incorporate environmental descriptors that are strongly associated with forest structure.

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