A comparison of ground cover and frequency estimation methods for post-harvest soil monitoring

The amount and type of ground cover is an important characteristic to measure when collecting soil disturbance monitoring data after a timber harvest. Estimates of ground cover and bare soil can be used for tracking changes in invasive species, plant growth and regeneration, woody debris loadings, and the risk of surface water runoff and soil erosion. A new method of assessing ground cover and soil disturbance was recently published by the U.S. Forest Service, the Forest Soil Disturbance Monitoring Protocol (FSDMP). This protocol uses the frequency of cover types in small circular (15cm) plots to compare ground surface in pre- and post-harvest condition. While both frequency and percent cover are common methods of describing vegetation, frequency has rarely been used to measure ground surface cover. In this study, three methods for assessing ground cover percent (step-point, 15cm dia. circular and 1x5m visual plot estimates) were compared to the FSDMP frequency method. Results show that the FSDMP method provides significantly higher estimates of ground surface condition for most soil cover types, except coarse wood. The three cover methods had similar estimates for most cover values. The FSDMP method also produced the highest value when bare soil estimates were used to model erosion risk. In a person-hour analysis, estimating ground cover percent in 15cm dia. plots required the least sampling time, and provided standard errors similar to the other cover estimates even at low sampling intensities (n=18). If ground cover estimates are desired in soil monitoring, then a small plot size (15cm dia. circle), or a step-point method can provide a more accurate estimate in less time than the current FSDMP method.

Inductive-Capacitive Resonant Circuit Sensors for Structural Health and Environmental Monitoring

Inductive-capacitive (LC) resonant circuit sensors are low-cost, wireless, durable, simple to fabricate and battery-less. Consequently, they are well suited to sensing applications in harsh environments or in situations where large numbers of sensors are needed. They are also advantageous in applications where access to the sensor is limited or impossible or when sensors are needed on a disposable basis. Due to their many advantages, LC sensors have been used for sensing a variety of parameters including humidity, temperature, chemical concentrations, pH, stress/pressure, strain, food quality and even biological growth. However, current versions of the LC sensor technology are limited to sensing only one parameter. The purpose of this work is to develop new types of LC sensor systems that are simpler to fabricate (hence lower cost) or capable of monitoring multiple parameters simultaneously. One design presented in this work, referred to as the multi-element LC sensor, is able to measure multiple parameters simultaneously using a second capacitive element. Compared to conventional LC sensors, this design can sense multiple parameters with a higher detection range than two independent sensors while maintaining the same overall sensor footprint. In addition, the two-element sensor does not suffer from interference issues normally encountered while implementing two LC sensors in close proximity. Another design, the single-spiral inductive-capacitive sensor, utilizes the parasitic capacitance of a coil or spring structure to form a single layer LC resonant circuit. Unlike conventional LC sensors, this design is truly planar, thus simplifying its fabrication process and reducing sensor cost. Due to the simplicity of this sensor layout it will be easier and more cost-effective for embedding in common building or packaging materials during manufacturing processes, thereby adding functionality to current products (such as drywall sheets) while having a minor impact on overall unit cost. These modifications to the LC sensor design significantly improve the functionality and commercial feasibility of this technology, especially for applications where a large array of sensors or multiple sensing parameters are required.

FACTORS AFFECTING DIPOREIA GROWTH RATES IN LAKE SUPERIOR

An ability to predict population dynamics of the amphipod Diporeia is important in understanding how energy pathways in the Lake Superior food web might be altered by disturbances to the ecosystem. Estimating growth rates for this prominent prey item for fish requires information on the physiological effects of changes to its environment. These effects have been investigated for Diporeia in other Great Lakes, but little is known about Lake Superior populations. The primary objective of this study is to obtain quantitative data for rates of Diporeia respiration and consumption that can be incorporated into a bioenergetics model for Lake Superior. Benthic communities in Lake Superior were sampled bimonthly from April through September during 2011 and 2012 to investigate spatial and temporal trends of Diporeia abundances as well as size class structures of the population. Additional samples of Diporeia were collected and kept alive in natural sediment for laboratory experiments. Respiration rates for Diporeia were measured by monitoring dissolved oxygen concentrations in microcosoms using microelectrodes. Additionally, a series of experiments to estimate consumption rates based on food availability were conducted using 14C-labeled algae (Selenastrum capricornutum). Amphipod population densities are highest between 30-110 m (slope) compared to 0-30 m (shelf) or >110 m (profundal) regions in Lake Superior. This heterogeneous distribution of Diporeia in Lake Superior is an important component to quantifying lake-wide biomass. Rates of oxygen consumption by Diporeia range from 32.0 to 44.7 mgO2*gDW-1*d-1, and do not vary significantly with body size per individual. The predicted consumption rate corresponding to average Lake Superior algal carbon fluxes was 0.08 ± SE mgC*gDW-1*d-1. Data on Lake Superior Diporeia biomass and bioenergetics found in this study can be incorporated in a model used to estimate the viability of this population under potential future environmental stressors.