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Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city
Antje Ahrends, Neil D. Burgess, Simon A. H. Milledge, Mark T. Bulling, Brendan Fisher, James C. R. Smart, G. Philip Clarke, Boniface E. Mhoro, Simon L. Lewis and Partha Sarathi Dasgupta
Proceedings of the National Academy of Sciences of the United States of America
Vol. 107, No. 33 (August 17, 2010), pp. 14556-14561
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/25708947
Page Count: 6
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Tropical forest degradation emits carbon at a rate of ∼0.5 Pg·y⁻¹, reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss. Putative patterns of degradation affecting forest stocks, carbon, and biodiversity have variously been described previously, but these have not been quantitatively assessed together or tested systematically. Economic theory predicts a systematic allocation of land to its highest use value in response to distance from centers of demand. We tested this theory to see if forest exploitation would expand through time and space as concentric waves, with each wave targeting lower value products. We used forest data along a transect from 10 to 220 km from Dar es Salaam (DES), Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y⁻¹, and an inner wave of lower value charcoal production 2 km·y⁻¹. This resource utilization is shown to reduce the public goods of carbon storage and species richness, which significantly increased with each kilometer from DES [carbon, 0.2 Mg·ha⁻¹; 0.1 species per sample area (0.4 ha)]. Our study suggests that tropical forest degradation can be modeled and predicted, with its attendant loss of some public goods. In sub-Saharan Africa, an area experiencing the highest rate of urban migration worldwide, coupled with a high dependence on forest-based resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity.
Proceedings of the National Academy of Sciences of the United States of America © 2010 National Academy of Sciences