Out of the three major cultural groups of Amazonia—tribal Amerindians, recent settlers form other areas and locally born non-Indians—the latter group is the most substantial, yet understudied. Chibnik considers the locally born, non-tribal residents in the tropical lowlands of Bolivia, Brazil and Peru in his attempt at finding workable definitions for the terminological categories used to describe them: caboclo, camba, cholo and ribereño. He reviews how anthropologists over the years have attempted to define the term, ‘ethnic group’ itself noting the particular challenge of distinguishing ethnicity from class. In trying to distill the meanings of the four ethnicities discussed here, Chibnik finds that occupational and regional associations bear significant influence on ethnic groupings. In some cases, he finds that boundaries and contrasts, especially concerning history and demography, can be more useful for defining a group than describing the group itself.

Providing the historical contexts of colonization per country, and using research methods that rely in part upon the analysis of last names, Chibnik outlines the dynamic and evolving meanings of each quasi-ethnic group, including the similarities and differences among them. The over-arching definition for a Brazilian caboclo in the Amazon is a poor, rural or urban, non-Indian and non-recent settler. A loose definition of a Peruvian ribereño sees them as exclusively rural, of any social class and non-Indian. A Peruvian cholo is a detribalized, acculturated, unassimilated Indian that does not generally marry out of his or her group. The term, cholo is regarded as derogatory and is not used for self-description. In Bolivia, camba has two meanings, either denoting anyone from the country’s eastern lowlands or signaling a lower-class, mestizo small farmer living near the city of Santa Cruz. The author concludes that conventional notions about what constitutes ethnicity are almost totally inapplicable to these groups.

Prepared by Megan Glore

Using the Brazilian Amazon as a case study, Perz and Skole critique and explore ways to improve upon a commonly referenced theory for understanding trends in reforestation and deforestation, called the forest transition theory. This broad theory leaves a number of important factors in land use and cover change unaccounted for, and Perz and Skole make up for this weakness by going into greater investigative and analytical detail using empirical data about the Amazon. Subdividing the immense study site into three different regions according to level of human settlement, the authors identified separate stages of the forest transition as being experienced by the differing subregions.

One shortcoming of the theory is stated to be its lack of attention to the differences between primary and secondary forests, i.e., the inherent discrepancies between biodiversity levels, carbon sequestration rates and economic benefits derivable. Another factor that limits application of the theory is its multi-century conception of forest regeneration, which neglects to analyze nuances in the process. Amazonian forests are characterized by short-run transition cycles, as discussed in the article, and forest transition theory cannot account for these complexities. Furthermore, social and economic conditions that generally precipitate forest cover declines and recoveries are not addressed by this theory, and the authors examine these influential factors for the Amazon case.

They point out the need for standardization of research methods and endorse the use of satellite, vegetation and social data for assessing forest cover. They suggest that integration of forest transition theory with other interdisciplinary social theories could improve our understanding of land cover change in tropical developing countries. While data indicates that the Brazilian Amazon is experiencing a rise in secondary forests but a net loss in total forest cover, the lack of an appropriate theoretical framework in which to situate these facts precludes the formulation of a grounded forecast for forest recovery.

Prepared by Megan Glore