Richardson, Andrew D.

https://dh.howard.edu/prom_corres/1123/thumbnail.jp

Biofilm-forming capability of highly virulent, multidrug-resistant Candida auris

The emerging multidrug-resistant yeast pathogen Candida auris has attracted considerable attention as a source of healthcare–associated infections. We report that this highly virulent yeast has the capacity to form antifungal resistant biofilms sensitive to the disinfectant chlorhexidine in vitro

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

Climate change at the ecosystem scale: a 50-year record in New Hampshire

Observing the full range of climate change impacts at the local scale is difficult. Predicted rates of change are often small relative to interannual variability, and few locations have sufficiently comprehensive long-term records of environmental variables to enable researchers to observe the fine-scale patterns that may be important to understanding the influence of climate change on biological systems at the taxon, community, and ecosystem levels. We examined a 50-year meteorological and hydrological record from the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, an intensively monitored Long-Term Ecological Research site. Of the examined climate metrics, trends in temperature were the most significant (ranging from 0.7 to 1.3 °C increase over 40–50 year records at 4 temperature stations), while analysis of precipitation and hydrologic data yielded mixed results. Regional records show generally similar trends over the same time period, though longer-term (70–102 year) trends are less dramatic. Taken together, the results from HBEF and the regional records indicate that the climate has warmed detectably over 50 years, with important consequences for hydrological processes. Understanding effects on ecosystems will require a diversity of metrics and concurrent ecological observations at a range of sites, as well as a recognition that ecosystems have existed in a directionally changing climate for decades, and are not necessarily in equilibrium with the current climate

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

The integration of on-line monitoring and reconfiguration functions using IEEE1149.4 into a safety critical automotive electronic control unit.

This paper presents an innovative application of IEEE 1149.4 and the integrated diagnostic reconfiguration (IDR) as tools for the implementation of an embedded test solution for an automotive electronic control unit, implemented as a fully integrated mixed signal system. The paper describes how the test architecture can be used for fault avoidance with results from a hardware prototype presented. The paper concludes that fault avoidance can be integrated into mixed signal electronic systems to handle key failure modes

Near-surface remote sensing of spatial and temporal variation in canopy phenology

There is a need to document how plant phenology is responding to global change factors, particularly warming trends. “Near-surface” remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras (“webcams”) can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for “regions of interest” within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface–atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux

Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems

In Ollinger et al. (1), we reported that mass-based concentrations of nitrogen in forest canopies (%N) are positively associated with whole-canopy photosynthetic capacity and canopy shortwave albedo in temperate and boreal forests, the latter result stemming from a positive correlation between %N and canopy near infrared (NIR) reflectance. This finding is intriguing because a functional link between %N and NIR reflectance could indicate an influence of nitrogen cycling on surface energy exchange, and could provide a means for estimating %N using broad-band satellite sensors

On quantifying the apparent temperature sensitivity of plant phenology.

Many plant phenological events are sensitive to temperature, leading to changes in the seasonal cycle of ecosystem function as the climate warms. To evaluate the current and future implications of temperature changes for plant phenology, researchers commonly use a metric of temperature sensitivity, which quantifies the change in phenology per degree change in temperature. Here, we examine the temperature sensitivity of phenology, and highlight conditions under which the widely used days-per-degree sensitivity approach is subject to methodological issues that can generate misleading results. We identify several factors, in particular the length of the period over which temperature is integrated, and changes in the statistical characteristics of the integrated temperature, that can affect the estimated apparent sensitivity to temperature. We show how the resulting artifacts can lead to spurious differences in apparent temperature sensitivity and artificial spatial gradients. Such issues are rarely considered in analyses of the temperature sensitivity of phenology. Given the issues identified, we advocate for process-oriented modelling approaches, informed by observations and with fully characterised uncertainties, as a more robust alternative to the simple days-per-degree temperature sensitivity metric. We also suggest approaches to minimise and assess spurious influences in the days-per-degree metric