Antonio G. Camacho | USGS Science

USGS2024

Human activities shape global patterns of decomposition rates in rivers

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities.

Science↗

USGS2021

Multi-geophysical parameter classification of the Montserrat geothermal system

Multi-geophysical parameter classification can help to reduce the uncertainties of interpretations that often rely on one geophysical technique. Integrating these varying datasets requires a more robust classification approach rather than traditional qualitative methods. In this study, we applied the Fuzzy c-means (FCM) method to quantitatively classify similarities in a high resolution seismic tomography, a magnetotellurics and gravity datasets obtained in Montserrat. To group similar datapoints, this application uses a Euclidean distance measure and a membership function. Assigned membership values indicate the degree to which a datapoint belongs to a specific class. The spatial distribution of the derived classes, each classified with distinct geophysical parameters, helped to provide new structural and petrological information of the Montserrat geothermal system. In comparison to previous models, our new cluster model highlights two major improvements. These include the resolution and assessment of the spatial extension and 3D geometry of previously undetected features within the Montserrat geothermal system and the constrain and characterization of earlier identified anomalies. We additionally utilized geological and petrological data obtained from three geothermal wells in the Montserrat geothermal system to help validate our classifications. Based on a semi-quantitative approach we assessed the reliability of the FCM technique in relation to the likely uncertainties of the different geophysical models.

Geothermics↗