5th UF Water Institute Symposium Abstract

Submitter's Name Geraldine Klarenberg
Session Name Poster Session - Watershed & Wetland Management
Poster Number 59
Author(s) Geraldine Klarenberg,  UF ABE (Presenting Author)
  Rafael Muñoz-Carpena,  UF ABE
  Stephen Perz, UF Sociology and Criminology & Law
  Ray Huffaker, UF ABE
  Determining Stochasticity and Causality of Vegetation Dynamics in the Southwestern Amazon
  Infrastructure projects such as road paving have proven to bring (mainly) socio-economic advantages to countries and populations. However, many studies have also highlighted the negative socio-economic and biophysical effects that these developments have at local, regional and even larger scales. The “MAP” area (Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia) is a biodiversity hotspot in the southwestern Amazon where sections of South America’s Inter-Oceanic Highway were paved between 2006 and 2010. We are interested in vegetation dynamics in the area since it plays an important role in ecosystem functions and ecosystem services in socio-ecological systems: it provides information on productivity and structure of the forest. In preparation of more complex and mechanistic simulation of vegetation, non-linear time series analysis and Dynamic Factor Analysis (DFA) was conducted on Enhanced Vegetation Index (EVI) time series - a remote sensing product which provides information on vegetation dynamics as it detects chlorophyll (productivity) and structural change. Time series of 30 years for EVI2 (from MODIS and AVHRR) and socio-economic and biophysical variables were obtained for 100 communities in the area. Through specific time series cluster analysis of the vegetation data, communities were clustered to facilitate data analysis and pattern recognition. The clustering is spatially consistent, and appears to be driven by median road paving progress – which differs per cluster. Non-linear time series analysis (multivariate singular spectrum analysis, MSSA) separates common signals (or low-dimensional attractors) across clusters. Despite the presence of this deterministic structure, we conclude vegetation time series behaves mostly stochastic. Granger causality analysis between EVI2 and all explanatory variables renders a causal ecological network, and cross-correlation indicates which variables (and with what lags) are to be included in DFA. This results in unique Dynamic Factor Models for each cluster, explaining vegetation dynamics with biophysical and socio-economic variables.