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Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Illicit Economies and Development (JIED) [ISSN:] 2516-7227 [Volume:] 6 [Issue:] 1 [Year:] 2024 [Pages:] 93-111
Verlag: 
LSE Press, London
Zusammenfassung: 
In recent decades, drug crop eradication and drug trafficking interdiction have pushed drug crop cultivation into new areas of the Amazonian rain forest. The presence of the drug industries in these regions-followed by alternative development programs that aim to substitute illicit drug crops with commodity crops like cacao-has transformed forest ecologies, risking loss to both biodiversity and cultural ecosystem services (CES) for surrounding communities. In the last ten years, forest loss linked to the increase in cultivation of commodity crops-both licit and illicit-has been monitored, generating extensive geospatial data. However, the spatiotemporal impacts on key plant species utilized by indigenous communities who have recently shifted to drug crop and commodity agricultural production remains poorly understood. In this paper, we use geospatial modeling to explore potential impacts of drug crop cultivation and alternative development programs on the CES of Ticuna indigenous communities of the Peruvian Amazon. We analyze the spatiotemporal impact of drug and commodity crop cultivation on three culturally significant species of the genus Ischnosiphon, known locally as dexpe or huarumá, by generating a model of potential distribution of the three species. The rate of increase of legal and illegal agricultural crops was also calculated and the spatiotemporal impact was measured and represented using spatial analysis techniques. Our analysis finds that, between 2010 and 2020, the increase in both illicit and licit commodity crop cultivation is correlated with changes in the distribution of huarumá species, which in turn affects the cultural ecosystem services of Ticuna communities.
Schlagwörter: 
biodiversity conservation
cultural ecosystem services
drug crop cultivation
Ticuna communities
Peruvian Amazon
geospatial modelling
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