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Shamanism or science?

Otto R Gottlieb¹, Maria Renata de M B Borin

¹Departamento de Fisiologia e Farmacodinâmica, FIOCRUZ, Rio de Janeiro, RJ, 21045-900, Brazil. ogottlieb@abc.org.br

Anais Da Academia Brasileira De Ciencias

|April 18, 2002

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Integration of ethnobotany and phytochemistry. dream or reality?

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This study integrates metabolism, morphology, and biogeography to enhance plant bioactivity understanding through ethnobotany. This holistic model connects organismic levels, offering predictive validity for ethnobotanical research.

Area of Science:

Integrative biology
Ethnobotany
Biogeography

Background:

Understanding plant bioactivity requires integrating diverse organismic levels.
Current models often lack a holistic approach connecting metabolism, morphology, and biogeography.
Ethnobotany offers a rich source of data but needs enhanced predictive power.

Purpose of the Study:

To develop a multipart architecture integrating metabolism, morphology, and biogeography.
To introduce plant bioactivity through ethnobotany-oriented descriptions within this architecture.
To establish predictive validity for ethnobotany using qualitative and quantitative models.

Main Methods:

Developing an integrative model connecting organismic levels.
Constructing qualitative and quantitative models using dahlgrenograms and Sporne indices.

Related Experiment Videos

Comparing a large ethnobotanical database (Pio Corrêa) with smaller databases from Amazonian Indian societies.

Main Results:

Demonstrated the coherence of the integrative model.
Validated the model's predictive capacity using comparative database analysis.
Showcased the successful integration of metabolism, morphology, and biogeography in understanding plant bioactivity.

Conclusions:

An integrative model is essential for a holistic understanding of organismic levels and plant bioactivity.
Ethnobotany, when integrated with evolutionary concepts and quantitative models, gains predictive validity.
This approach provides a realistic scheme for connecting diverse biological data.