Forest structure predicts plant and animal species diversity and composition changes in an Amazonian forest

Author:

Pedro Marcelle São1,Smith Marielle N.2,Zuquim Gabriela3,Tuomisto Hanna3,Stark Scott C.2,Amaral Lucas4,Bobrowiec Paulo Estefano D.5,Bueno Anderson S.6,Capaverde Ubirajara7,Castilho Carolina8,Esteban Erick1,Lima Albertina1,Magnusson William1,Menger Juliana9,Pinto Maria Goretti1,Rincón Lorena1,Tavares Valéria da Cunha5,Waldez Fabiano10,Schietti Juliana4

Affiliation:

1. National Institute of Amazonian Research

2. Michigan State University

3. University of Turku

4. Universidade Federal do Amazonas

5. Instituto Tecnológico Vale

6. Instituto Federal de Educação, Ciência e Tecnologia Farroupilha, Júlio de Castilhos

7. Secretaria de Estado de Segurança Pública de Roraima

8. Empresa Brasileira de Pesquisa Agropecuária

9. Helmholtz Centre for Environmental Research

10. Instituto Federal de Educação, Ciência e Tecnologia do Amazonas

Abstract

Abstract

Forest structure plays an important role in determining habitat suitability for plants and animals, but these relationships are poorly characterized for different biological communities in tropical forests. We used ground-based lidar to quantify structural metrics and determine their contribution in predicting species diversity and compositional changes between plots for nine biological groups in an Amazonian forest. For each group, we calculated Fisher's alpha index and summarized community composition using Principal Coordinates Analysis. As biological organisms may also react directly to hydro-edaphic conditions, we carried out variation partitioning analysis using linear regressions to disentangle the relative contribution of structural metrics and hydro-edaphic variables. Forest structure was related to species diversity and composition of some groups, specifically for plants, anurans, and birds. Mean canopy height, leaf area height volume, and skewness explained more than one-third of species diversity of palms and trees, with higher values relating to higher species diversity. Hydro-edaphic variables were the most important predictors of the main compositional axis for plant groups, but some structural metrics explained more than 30% of the secondary compositional axis for ferns + lycophytes, trees, birds, and anurans. Vegetation height and variability, vegetation quantity, and vertical structure, but not canopy openness, were the main structural characteristics modulating species diversity and composition. Our findings reinforce the potential to estimate species diversity and compositional changes across structural gradients using lidar-derived metrics in a hyper-diverse forest. Understanding these relationships advances our ability to make community predictions useful for conservation and provides new avenues to investigate the mechanisms impacting diversity.

Publisher

Springer Science and Business Media LLC

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