Estimating trend precision and power to detect trends across grouped count data

Ecologists commonly use grouped or clustered count data to estimate temporal trends in counts, abundance indices, or abundance. For example, the U.S. Breeding Bird Survey data represent multiple counts of birds from within each of multiple, spatially defined routes. Despite a reliance on grouped counts, analytical methods for prospectively estimating precision of trend estimates or statistical power to detect trends that explicitly acknowledge the characteristics of grouped count data are undescribed. These characteristics include the fact that the sampling variance is an increasing function of the mean, and that sampling and group-level variance estimates are generally estimated on different scales (the sampling and log scales, respectively). We address these issues for repeated sampling of a single population using an analytical approach that has the flavor of a generalized linear mixed model, specifically that of a negative binomial-distributed count variable with random group effects. The count mean, including grand intercept, trend, and random group effects, is modeled linearly on the log scale, while sampling variance of the mean is estimated on the log scale via the delta method. Results compared favorably with those derived using Monte Carlo simulations. For example, at trend = 5% per temporal unit, differences in standard errors and in power were modest relative to those estimated by simulation (< or = /11/% and < or = /16/%, respectively), with relative differences among power estimates decreasing to < or = /7/% when power estimated by simulations was > or = 0.50. Similar findings were obtained using data from nine surveys of fingernail clams in the Mississippi River. The proposed method is suggested (1) where simulations are not practical and relative precision or power is desired, or (2) when multiple precision or power calculations are required and where the accuracy of a fraction of those calculations will be confirmed using simulations.     

Windows of opportunity: historical and ecological controls on Berberis thunbergii invasions

Attempts to determine characteristics that render habitats invasible to nonnative species have met with limited success. This may be because most studies focus on modern habitat conditions and do not consider invasibility in the context of a historically dynamic landscape in which both the abundance of a species and the invasibility of a site may change. We surveyed 159 currently forested sites for the occurrence and abundance of Berberis thunbergii (Japanese barberry), an invasive, nonnative shrub in forests of the northeastern United States, relative to modern environmental conditions, contemporary logging activity, and two periods of historical land use. Berberis thunbergii occurred more frequently and was more abundant in post-agricultural forests than in continuously wooded sites. This relationship was stronger for agricultural sites that were abandoned and reforested after B. thunbergii was introduced to the region than for sites that reforested prior to B. thunbergii introduction. In contrast, recent forest harvesting did not influence the occurrence or abundance of B. thunbergii. Modern soil fertility explained a significant portion of the variation in B. thunbergii occurrence, whereas site history considerably improved predictions of population density and helped evaluate potential invasion mechanisms. While land-use history covaries with soil fertility and distance to putative seed sources, the strong relationship between modern abundance patterns and historical agriculture suggests that B. thunbergii colonized recently abandoned agricultural lands in the early 20th century and then persisted and spread locally during subsequent reforestation. Our results indicate that interpretations of both native community composition and modern plant invasions must consider the importance of historical landscape changes and the timing of species introduction along with current environmental conditions.     

Source identification and assessment of sediment contamination of trace metals in Kogarah Bay,NSW, Australia

The distribution of trace metals (spatial and temporal) and sedimentary fractions were investigated to identify the concentrations and sources of trace metals within Kogarah Bay, NSW, Australia. A total of 59 surface sediments and six subsurface samples from core of the sediment were collected. The contamination factor and pollution load index indices used to evaluate environmental effects of trace metals. The study area was found to be uncontaminated with Cr and Ni, moderately contaminated with As and considerably contaminated with Cu, Zn and Pb. The concentrations of Cr and Ni were below both effect range low and effect range median, while As, Cu, Zn and Pb were slightly above effect range low. The highest concentrations of these trace metals such as Cu, Zn and Pb were found in the north, northwest and southeast of the bay, close to discharge points, stormwater outlets and around boatyards and watercrafts. The spatial distributions of metals were strongly related to muddy particles and organic matter. The temporal sediments of metals declined with increased sediment depth, which reflects accumulation of trace metals since European settlement in this area. Furthermore, the source of the trace metals was found to be stormwater outlets, gasoline fumes, boatyards and other human activities.     

A review of source tracking techniques for fine sediment within a catchment

Excessive transport of fine sediment, and its associated pollutants, can cause detrimental impacts in aquatic environments. It is therefore important to perform accurate sediment source apportionment to identify hot spots of soil erosion. Various tracers have been adopted, often in combination, to identify sediment source type and its spatial origin; these include fallout radionuclides, geochemical tracers, mineral magnetic properties and bulk and compound-specific stable isotopes. In this review, the applicability of these techniques to particular settings and their advantages and limitations are reviewed. By synthesizing existing approaches, that make use of multiple tracers in combination with measured changes of channel geomorphological attributes, an integrated analysis of tracer profiles in deposited sediments in lakes and reservoirs can be made. Through a multi-scale approach for fine sediment tracking, temporal changes in soil erosion and sediment load can be reconstructed and the consequences of changing catchment practices evaluated. We recommend that long-term, as well as short-term, monitoring of riverine fine sediment and corresponding surface and subsurface sources at nested sites within a catchment are essential. Such monitoring will inform the development and validation of models for predicting dynamics of fine sediment transport as a function of hydro-climatic and geomorphological controls. We highlight that the need for monitoring is particularly important for hilly catchments with complex and changing land use. We recommend that research should be prioritized for sloping farmland-dominated catchments.     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Feedbacks in Human–Landscape Systems

This article identifies key questions and challenges for geomorphologists in investigating coupled feedbacks in human–landscape systems. While feedbacks occur in the absence of human influences, they are also altered by human activity. Feedbacks are a key element to understanding human-influenced geomorphic systems in ways that extend our traditional approach of considering humans as unidirectional drivers of change. Feedbacks have been increasingly identified in Earth-environmental systems, with studies of coupled human–natural systems emphasizing ecological phenomena in producing emerging concepts for social–ecological systems. Enormous gaps or uncertainties in knowledge remain with respect to understanding impact-feedback loops within geomorphic systems with significant human alterations, where the impacted geomorphic systems in turn affect humans. Geomorphology should play an important role in public policy by identifying the many diffuse and subtle feedbacks of both local- and global-scale processes. This role is urgent, while time may still be available to mitigate the impacts that limit the sustainability of human societies. Challenges for geomorphology include identification of the often weak feedbacks that occur over varied time and space scales ranging from geologic time to single isolated events and very short time periods, the lack of available data linking impact with response, the identification of multiple thresholds that trigger feedback mechanisms, the varied tools and metrics needed to represent both physical and human processes, and the need to collaborate with social scientists with expertise in the human causes of geomorphic change, as well as the human responses to such change.