Infrastructure development, specifically road paving, contributes socio-economic benefits to society worldwide. However, detrimental environmental effects of road paving have been documented, most notably increased deforestation. Beyond deforestation, we hypothesize that road paving introduces "unseen" regional scale effects on forests, due to changes to vegetation dynamics. To test this hypothesis, we focus on the tri-national frontier in the southwestern Amazon that has been subject to construction of the Inter-Oceanic Highway (IOH) between 1987 and 2010. We use a long-term remotely sensed vegetation index as a proxy for vegetation dynamics and combine these with field-based socio-ecological data and biophysical data from global datasets. We find 4 areas of shared vegetation dynamics associated with increasing extent of road paving. Applying Dynamic Factor Analysis, an exploratory dimension-reduction time series analysis technique, we identify common trends and covariates in each area. Common trends, indicating underlying unexplained effects, become relatively less important as paving increases, and covariates increase in importance. The common trends are dominated by lower frequency signals possibly embodying long-term climate variability. Human-related covariates become more important in explaining vegetation dynamics as road paving extent increases, particularly family density and travel time to market. Natural covariates such as minimum temperature and soil moisture become less important. The change in vegetation dynamics identified in this study indicates a possible change in ecosystem services along the disturbance gradient. While this study does not include all potential factors controlling dynamics and disturbance of vegetation in the region, it offers important insights for management and mitigation of effects of road paving projects. Infrastructure planning initiatives should make provisions for more detailed vegetation monitoring after road completion, with a broader focus than just deforestation. The study highlights the need to mitigate population-driven pressures on vegetation like family density and access to new markets.

Road construction and paving bring socio-economic benefits to receiving regions but can also be drivers of deforestation and land cover change. Road infrastructure often increases migration and illegal economic activities, which in turn affect local hydrology, wildlife, vegetation structure and dynamics, and biodiversity. To evaluate the full breadth of impacts from a coupled natural-human systems perspective, information is needed over a sufficient timespan to include pre- and post-road paving conditions. In addition, the spatial scale should be appropriate to link local human activities and biophysical system components, while also allowing for upscaling to the regional scale. A database was developed for the tri-national frontier in the Southwestern Amazon, where the Inter-Oceanic Highway was constructed through an area of high biological value and cultural diversity. Extensive socio-economic surveys and botanical field work are combined with remote sensing and reanalysis data to provide a rich and unique database, suitable for coupled natural-human systems research.

In 2013, the National Ecological Observatory Network (NEON) started collecting 30-year multi-faceted ecological data at various spatial and temporal scales across the US including ticks. Understanding the abundance and dynamics of disease vectors under changing environmental conditions in the long-term is important to societies, but sustained long-term collection efforts are sparse. Using hard-bodied tick data collected by NEON, the vegetation and atmospheric data and a statistical state-space model, which included a detection probability component, this study estimated the abundance of tick nymphs and adult ticks across a Florida NEON location. It took into account the spatial and temporal variation, and factors affecting detection. Its purpose was to test the applicability of data collected thus far and evaluate tick abundance. The study found an increase in tick abundance at this Florida location, and was able to explain spatial and temporal variability in abundance and detection. This approach shows the potential of NEON data. The NEON data collection is unique in scale, and promises to be of great value to understand tick and disease dynamics across the US. From a public health perspective, the detection probability of vectors can be interpreted as the probability of encountering that vector, making these types of analyses useful for estimating disease risk.