A deep-time socioecosystem framework to understand social vulnerability on a tropical island

TitleA deep-time socioecosystem framework to understand social vulnerability on a tropical island
Publication TypeJournal Article
Year of Publication2018
AuthorsRivera-Collazo I.C, Rodriguez-Franco C., Garay-Vazquez J.J
Volume23
Pagination97-108
Date Published2017/11
Type of ArticleArticle
ISBN Number1461-4103
Accession NumberWOS:000419076600010
Keywordsarchaeology; deep-time perspective; environmental archaeology; environmental change; panarchy; Puerto Rico; puerto-rico; resilience theory; Restoration; Social vulnerability; socioecosystem dynamics
Abstract

Archaeological research has the potential to contribute to our understanding of social vulnerability to environmental change by providing examples of change in the deep and recent past. Here we argue that human activity and historical processes deeply transform tropical environments through time, and that these changes accumulate on the landscape affecting social vulnerability. These changes, however, are not always evident due to rapid vegetation growth obscuring past human impact. Our research investigates the northernmost 25 km of the Manati Hydrological Basin in Puerto Rico, focusing on evidence of human activity and environmental characteristics including topography, sediments and vegetation cover. The data collected, which articulates archaeological and ethnographic records, covers the span of pre-Columbian occupation of the region, through the colonial periods, and into the twentieth century. Results show that human activity through time has deeply altered the forests. The accumulation of long-term histories of biotic, abiotic and cultural dynamics affects social sensitivity and exposure. Human ingenuity can widen resilience thresholds, making long-term practices particularly important components of adaptive strategies. Deep-time socioecological perspectives can contribute to current vulnerability assessments by enhancing local and historical records that can feed predictive models and inform decision-making in the present.

DOI10.1080/14614103.2017.1342397
Student Publication: 
No
Research Topics: 
sharknado