Cluster and Discriminant Analyses for Stem

Abstract

The diversity of species in native tropical forests causes difficulty in the interpretation of data that support their management and conservation. Species grouping, based on characteristics of interest, reduces significantly the number of volume equations and helps solve the problem of undersampling rare species. This study aims to group 32 Amazonian trees species of commercial interest based on regression coefficients of the Schumacher and Hall's model and their fit statistics. To accomplish this, we employ a two-stage approach, in which we first applied cluster analysis to classify species with higher sampling intensity (n > 30). This phase allowed us to allocate poorly sampled species (n < 30) to groups created by discriminant analysis, resulting in the second stage. This proposed approach has proven adequate for grouping timber species in the Amazon forest, and so the stem volume can be modelled on consistent groups of species. The grouping of Amazon rainforest commercial species, based on the regression coefficients and fit statistics, performs better in aggregation for the stem volume modelling, providing stabilisation of estimation error and supplying few equations for the evaluation of standing stock.

Multilevel mixed-effect models to predict wood volume in a hyperdiverse Amazon forest

“…Second, rare species, which are common in the Amazon forest (Schulze et al 2008) are limiting for species-specific model fitting (Cysneiros et al 2017a). Grouping species by size may solve this issue, but a large sample size is required to reliably classify new species into a size group (Cysneiros et al 2017b). However, mixed models are efficient only when random effects are expressive (Vismara et al 2015), i.e., if a random effect is similar to the population mean no benefit will be observed in using these models, and the fixed-effects models may be more appropriate (Colmanetti et al 2020).…”

Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach.

“…Thus, tree samples have few representatives by species, and the usual per species modelling approach, applied in temperate forests, is inefficient. One solution is to cluster species and to model physiological processes at species cluster levels (Cysneiros et al, 2017; Lahoreau et al, 2002).…”

Análise da produção de madeira em área de concessão florestal na Amazônia ocidental: um estudo de caso na floresta nacional do Jamari

“…A Tabela 1 é possível observar que, dentre as espécies selecionadas, o volume movimentado pelo grupo G2 representou apenas 2,5% do volume total movimentado em relação ao grupo G1. Das 15 espécies selecionadas, 11 também foram selecionadas porCysneiros et al (2017) ao estudar a diversidade de espécies de madeira nas florestas tropicais nativas e dificuldade na interpretação dos dados que sustentam seu manejo e conservação, onde agrupou 32 espécies da Amazônia consideradas de interesse comercial.…”