Identification of characteristic plant co-occurrences in neotropical secondary montane forests
1 Max Planck Institute for Biogeochemistry, PO Box 10 01 64, 07701 Jena, Germany
2 Department of Environmental Sciences, Swiss Federal Institute of Technology—ETH Zurich, 8092 Zurich, Switzerland
3 Bayreuth Centre for Ecology and Environmental Research, University of Bayreuth, 95440 Bayreuth, Germany
4 Fundación Cordillera Tropical, Jaime Roldós 4-80 y Huayna Cápac, Ed. El Consorcio of. 412, Cuenca, Ecuador
5 Norwegian Forest and Landscape Institute, PO Box 115, 1431 Ås, Norway
* Correspondence address: Biogeochemical Model-Data Integration Group, Max Planck Institute for Biogeochemistry, PO Box 10 01 64, 07701 Jena, Germany. Tel: +49-3641-576282; Fax: +49-3641-577200; E-mail: miguel.mahecha{at}bgc-jena.mpg.de
Aims: Inferring environmental conditions from characteristic patterns of plant co-occurrences can be crucial for the development of conservation strategies concerning secondary neotropical forests. However, no methodological agreement has been achieved so far regarding the identification and classification of characteristic groups of vascular plant species in the tropics. This study examines botanical and, in particular, statistical aspects to be considered in such analyses. Based on these, we propose a novel data-driven approach for the identification of characteristic plant co-occurrences in neotropical secondary mountain forests.
Methods: Floristic inventory data were gathered in secondary tropical mountain forests in Ecuador. Vegetation classification was performed by coupling locally adaptive isometric feature mapping, a non-linear ordination method and fuzzy-c-means clustering. This approach was designed for dealing with underlying non-linearities and uncertainties in the inventory data.
Important Findings: The results indicate that the applied non-linear mapping in combination with fuzzy classification of species occurrence allows an effective identification of characteristic groups of co-occurring species as fuzzy-defined clusters. The selected species indicated groups representing characteristic life-form distributions, as they correspond to various stages of forest regeneration. Combining the identified ‘characteristic species groups’ with meta-information derived from accompanying studies indicated that the clusters can also be related to habitat conditions.
In conclusion, we identified species groups either characteristic of different stages of forest succession after clear-cutting or of impact by fire or a landslide. We expect that the proposed data-mining method will be useful for vegetation classification where no a priori knowledge is available.
Keywords: secondary tropical mountain forests • characteristic species groups • non-linear ordination • fuzzy-c-means clustering • Ecuador