Geology topics

Ruth D. Yanai

Publications and source records attributed to Ruth D. Yanai.

3 recordsLinked to original sources

USGS2026

Cross-site comparison of ecosystem nutrient cycling in hardwood and conifer forests on contrasting soils

In the northeastern United States, both hardwood and conifer forests have developed on sites with contrasting soils, allowing an examination of the effect of site and forest type on ecosystem nutrient cycling. We measured biomass production and nutrient fluxes in northern hardwood and conifer stands at three sites differing in soil fertility. We found that leaf, root, and wood concentrations of calcium (Ca), magnesium (Mg), and potassium reflected differences in soil base cation availability, while concentrations of nitrogen (N) and phosphorus (P) were more consistent across sites. Nutrient uptake was calculated as the sum of litterfall, net throughfall (throughfall minus precipitation), root turnover, and accumulation in perennial tissues (wood). We propose a novel metric of nutrient cycling, the nutrient retention fraction (NRF), defined as the proportion of annual nutrient uptake retained in biomass accretion. Because the NRF is unitless, it can be compared across nutrients; Ca and Mg had the highest NRF and P the lowest ( p = 0.05). Across sites and elements, NRFs were lower for conifers (5.0 ± 0.6%) than for hardwoods (7.2 ± 0.5%), associated with their lower productivity. Nutrient-use efficiency (biomass production divided by nutrient uptake) tended to be high where foliar concentrations indicated low availability of that nutrient. Nutrient retention of N and P was higher where availability of the other element was high, which could be a mechanism contributing to N and P co-limitation.

Canadian Journal of Forest Research↗

USGS2016

Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watershed

Estimating streamwater solute loads is a central objective of many water-quality monitoring and research studies, as loads are used to compare with atmospheric inputs, to infer biogeochemical processes, and to assess whether water quality is improving or degrading. In this study, we evaluate loads and associated errors to determine the best load estimation technique among three methods (a period-weighted approach, the regression-model method, and the composite method) based on a solute's concentration dynamics and sampling frequency. We evaluated a broad range of varying concentration dynamics with stream flow and season using four dissolved solutes (sulfate, silica, nitrate, and dissolved organic carbon) at five diverse small watersheds (Sleepers River Research Watershed, VT; Hubbard Brook Experimental Forest, NH; Biscuit Brook Watershed, NY; Panola Mountain Research Watershed, GA; and Río Mameyes Watershed, PR) with fairly high-frequency sampling during a 10- to 11-yr period. Data sets with three different sampling frequencies were derived from the full data set at each site (weekly plus storm/snowmelt events, weekly, and monthly) and errors in loads were assessed for the study period, annually, and monthly. For solutes that had a moderate to strong concentration–discharge relation, the composite method performed best, unless the autocorrelation of the model residuals was <0.2, in which case the regression-model method was most appropriate. For solutes that had a nonexistent or weak concentration–discharge relation (model R 2 < about 0.3), the period-weighted approach was most appropriate. The lowest errors in loads were achieved for solutes with the strongest concentration–discharge relations. Sample and regression model diagnostics could be used to approximate overall accuracies and annual precisions. For the period-weighed approach, errors were lower when the variance in concentrations was lower, the degree of autocorrelation in the concentrations was higher, and sampling frequency was higher. The period-weighted approach was most sensitive to sampling frequency. For the regression-model and composite methods, errors were lower when the variance in model residuals was lower. For the composite method, errors were lower when the autocorrelation in the residuals was higher. Guidelines to determine the best load estimation method based on solute concentration–discharge dynamics and diagnostics are presented, and should be applicable to other studies.

Ecosphere↗

USGS2014

Evaluating the efficiency of environmental monitoring programs

Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.

Ecological Indicators↗