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Ellen Jasinski, Douglas Morton, Ruth DeFries, Yosio Shimabukuro, Liana Anderson, and Matthew Hansen

regressions Two multiple logistic regressions (MLRs) were performed using SAS statistical software in order to determine relationships between the selected physical variables and probabilities of conversion to mechanized agriculture. Logistic regression was chosen for its utility in analyses with dichotomous dependent variables. Previous studies have demonstrated its usefulness for examining causes as well as effects of land-use and land-cover change ( Ludeke et al. 1990 ; Osborne et al. 2001 ; Serneels

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Carlos M. Souza Jr., Dar A. Roberts, and AndréL. Monteiro

1. Introduction Selective logging, fragmentation, and forest burning are the main factors contributing to forest degradation of the Brazilian Amazon. The major impacts of these anthropogenic disturbances include decreasing forest biomass ( Cochrane and Schulze 1999 ; Gerwing 2002 ), creating favorable environments for nonnative species ( Vidal et al. 1997 ), and causing local species extinctions ( Martini et al. 1994 ). It has been estimated through field surveys and socioeconomic interviews

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Ted R. Feldpausch, Susan J. Riha, Erick C. M. Fernandes, and Elisa V. Wandelli

various states of management, degradation, abandonment, or spontaneous forest recovery. Primary forest conversion to pasture differs from traditional slash-and-burn agriculture in that the patch of land cleared, the intensity of disturbance, and the time interval of use are greater for pasture ( Uhl et al. 1988 ). Forest recovery following slash-and-burn agriculture, the predominant historical anthropogenic disturbance in the Amazon basin, has been well studied ( Ewel 1971 ; Uhl et al. 1981 ; Uhl

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