Insights from a large-scale inventory in the southern Brazilian Atlantic Forest
Sci. agric
; 77(1): e20180036, 2020. map, ilus, graf
Article
em En
| VETINDEX
| ID: biblio-1497829
Biblioteca responsável:
BR68.1
Localização: BR68.1
ABSTRACT
A key issue in large-area inventories is defining a suitable sampling design and the effort required to obtain reliable estimates of species richness and forest attributes, especially in species-diverse forests. To address this issue, data from 418 systematically distributed 0.4 ha plots were collected. Estimators of nonparametric species richness were employed to assess the floristic representativeness of data collected in three forest types in the Brazilian Atlantic Forest. The sampling sufficiency of forest attributes was evaluated as a function of sample size. Altogether, 831 tree/shrub species were recorded. The data acquired through the systematic sampling design were representative of both species richness and basal area. The confidence intervals length would not substantially decrease by using more than 70 % of the reference sample (n = 364), thereby reaching a length of ∼5 % of the sample mean. Nevertheless, reliable estimates of species richness for diverse forests demand a thorough sampling approach far more exacting so as to achieve acceptable population estimates of forest attributes. Though the study area is regarded as a biodiversity hotspot, the forest stands showed diminished species richness, basal area, stem volume and biomass when compared to old-growth stands. As regards species richness, the data provided evidence of contrasting great γ-diversity (at the forest type level) and small α-diversity (at the forest stand level). Amongst anthropic impacts, illegal logging and extensive cattle grazing within stands are undoubtedly key factors that threaten forest conservation in the study area.
Palavras-chave
Texto completo:
1
Base de dados:
VETINDEX
Assunto principal:
Árvores
/
Florestas
/
Biodiversidade
País/Região como assunto:
America do sul
/
Brasil
Idioma:
En
Revista:
Sci. agric
Ano de publicação:
2020
Tipo de documento:
Article