Compost has been evaluated as a stormwater best management practice for erosion control, but site revegetation is the ultimate goal of most stormwater plans. In this study, three different composts applied as a surface layer or mulch at two depths of 5 and 10 cm were compared with topsoil and subsoil as a medium for crop growth and weed suppression during revegetation of a highway right-of-way. Compost was shown to be as effective as topsoil and subsoil controls for crop growth, while significantly reducing growth of weed species. There were no significant differences between 5- and 10-cm depths of composts, indicating that the shallower depth would be adequate for establishing a cover crop and achieving weed suppression. Compost mulches offer promising opportunities for crop and weed management during revegetation of roadsides and other disturbed landscapes.

Spatial environmental data sometimes include below detection limit observations (i.e. censored values reported as less than a level of detection). Historically, the most common practice for analysis of such data has been to replace the censored observations with some function of the level of detection (LOD), like LOD/2. We show that estimates and standard errors found using this single substitution method are biased. In particular, the spatial variance and variability in estimation is underestimated. We develop a measurement error Bayesian spatial model for the analysis of spatial data with censored values. Parameter estimation and predictions at observed and unobserved locations are computed using a data augmentation method using a Markov chain Monte Carlo algorithm. The data augmentation method is illustrated using data from a dioxin contaminated site in Missouri. We also use simulation to investigate the small sample properties of predictions and parameter estimates and the robustness of the data augmentation method.

Pasturelands are inherently variable. It is this variability that makes sampling as well as characterizing an entire pasture difficult. Measurement of plant canopy reflectance with a ground-based radiometer offers an indirect, rapid, and noninvasive characterization of pasture productivity and composition. The objectives of this study were (i) to determine the relationships between easily collected canopy reflectance data and pasture biomass and species composition and (ii) to determine if the use of pasture reflectance data as a covariate improved mapping accuracy of biomass, percentage of grass cover, and percentage of legume cover across three sampling schemes in a central Iowa pasture. Reflectance values for wavebands most highly correlated with biomass, percentage of grass cover, and percentage of legume cover were used as covariates. Cokriging was compared with kriging as a method for estimating these parameters for unsampled sites. The use of canopy reflectance as a covariate improved prediction of grass and legume percentage of cover in all three sampling schemes studied. The prediction of above-ground biomass was not as consistent given that improvement with cokriging was observed with only one of the sampling schemes because of the low amount of spatial continuity of biomass values. An overall improvement in root mean square error (RMSE) for predicting values for unsampled sites was observed when cokriging was implemented. Use of rapid and indirect methods for quantifying pasture variability could provide useful and convenient information for more accurate characterization of time consuming parameters, such as pasture composition.

The probability of a rare event is usually estimated directly as the number of times the event occurs divided by the total sample size. Unfortunately, the precision of this estimate is low. For typical sample sizes of N < 100 in ecological studies, the coefficient of variation (cv) of this estimate of the probability of a rare event can exceed 300%. Sample sizes on the order of 10³–10⁴ observations are needed to reduce the cv to below 10%. If it is impractical or impossible to increase the sample size, auxiliary data can be used to improve the precision of the estimate. We describe four approaches for using auxiliary data to improve the precision of estimates of the probability of a rare event: (1) Bayesian analysis that includes prior information about the probability; (2) stratification that incorporates information on the heterogeneity in the population; (3) regression models that account for information correlated with the probability; and (4) inclusion of aggregated data collected at larger spatial or temporal scales. These approaches are illustrated using data on the probability of capture of vespulid wasps by the insectivorous plant Darlingtonia californica. All four methods increase the precision of the estimate relative to the simple frequency-based estimate (absolute precision = 1.26, relative precision [cv] = 70%): stratification (absolute precision = 1.10, cv = 62%); regression models (absolute precision = 1.59, cv = 55%); Bayesian analysis with an informative prior probability distribution (absolute precision = 4.28, cv = 47%); and using temporally aggregated data (absolute precision = 6.75, cv = 36%). When informative auxiliary data is available, we recommend including it when estimating the probability of rare events.

In spite of the title, this is really a textbook for a one‐semester, introductory statistics course for biologists. It covers the standard material: descriptive statistics, concepts of sampling, inference and testing, one‐ and two‐sample Chi‐square tests, one‐, two‐, and k‐sample tests of location, regression, and correlation. Both parametric methods (e.g., one‐way ANOVA) and nonparametric methods (e.g., the Kruskal‐Wallis test) are presented. The style is very conversational and nonmathematical. Methods are illustrated using biological examples. Instructions are given for both hand calculation and the SPSS package. Each chapter includes self‐help questions, with answers at the back of the volume, but there are no homework problems. Assignments from the author’s course at Anglia Ruskin University and other supporting material are included on a companion website, although some of the material is password protected and available only to instructors who adopt the book for their course.

Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.

The usual statistical tests of trend are inappropriate for demonstrating the absence of trend. This is because failure to reject the null hypothesis of no trend does not prove that null hypothesis. The appropriate statistical method is based on an equivalence test. The null hypothesis is that the trend is not zero, i.e., outside an a priori specified equivalence region defining trends that are considered to be negligible. This null hypothesis can be tested with two one-sided tests. A proposed equivalence region for trends in population size is a log-linear regression slope of (−0.0346, 0.0346). This corresponds to a half-life or doubling time of 20 years for population size. A less conservative region is (−0.0693, 0.0693), which corresponds to a halving or doubling time of 10 years. The approach is illustrated with data on four amphibian populations; one provides significant evidence of no trend.

Hypericum perforatum (Hp) is commonly known for its antiviral, antidepressant, and cytotoxic properties, but traditionally Hp was also used to treat inflammation. In this study, the anti-inflammatory activity and cytotoxicity of different Hp extractions and accessions and constituents present within Hp extracts were characterized. In contrast to the antiviral activity of Hp, the anti-inflammatory activity observed with all Hp extracts was light-independent. When pure constituents were tested, the flavonoids, amentoflavone, hyperforin, and light-activated pseudohypericin, displayed anti-inflammatory activity, albeit at concentrations generally higher than the amount present in the Hp extracts. Constituents that were present in the Hp extracts at concentrations that inhibited the production of prostaglandin E₂ (PGE₂) were pseudohypericin and hyperforin, suggesting that they are the primary anti-inflammatory constituents along with the flavonoids, and perhaps the interactions of these constituents and other unidentified compounds are important for the anti-inflammatory activity of the Hp extracts.

We examined patterns of nutrient heterogeneity in the mineral soil (0–15 cm depth) of 13 southern Appalachian forest stands in western North Carolina >60 yr after abandonment from pasture or timber harvest to investigate the long-term effects of land use on the spatial distribution and supply of soil resources. We measured soil carbon (C), nitrogen (N), acid-extractable phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations and pools, and potential net N mineralization and nitrification rates to evaluate differences in mean values, variance at multiple scales, and fine-scale spatial structure.

While comparisons of averaged values rarely indicated that historical land use had an enduring effect on mineral soil or N cycling, differences in variance and spatial structure suggested that former activities continue to influence nutrient distributions by altering their spatial heterogeneity. Patterns differed by element, but generally variance of soil C, N, and Ca decreased and variance of soil P, K, and Mg increased with intensive past land use. Changes in variance were most conspicuous and consistent locally (<28 m), but C, Ca, P, and Mg also exhibited appreciable differences in variance at coarser scales (>150 m). High variability in soil compaction resulted in some changes in scale-dependent patterns of nutrient pool variance compared with nutrient concentration variance. It also affected the variance of N cycling rates, such that mass-based rates varied less and area-based rates varied more in intensively used areas than in reference stands. Geostatistical analysis suggested that past land use homogenized the spatial structure of soil C, K, and P in former pastures. In contrast, logged stands had highly variable spatial patterning for Ca.

These results suggest that land use has persistent, multi-decadal effects on the spatial heterogeneity of soil resources, which may not be detectable when values are averaged across sites. By interacting with patterns of variability in the plant and heterotrophic biota, differences in nutrient distribution and supply could alter the composition and diversity of forest ecosystems. Scale-dependent changes in nutrient heterogeneity could also complicate efforts to determine biogeochemical budgets and cycling rates.

Control of stormwater runoff and soil erosion on highway construction sites is a concern for state departments of transportation and municipalities. Composted organics are viewed as an alternative approach to improve construction site soils and to reduce runoff and soil erosion. The objective of this study is to evaluate the use of blanket-applied composted organics on rill erosion as compared to soils. Rill erosion was measured on three composted organics applied at 5 and 10 cm depths, a topsoil treatment (15 cm application), and the existing soil (control) on a highway embankment with a three-to-one sideslope (33%). Treatments were tested using rainfall simulation at a target rate of 100 mm/h and simultaneously adding five inflows at the top of the rill on both vegetated and unvegetated plots. Rill erosion on blanket-applied compost treatments was measured, and the usefulness of the shear stress model for predicting rill erosion on compost-treated areas was assessed. Rill erodibilities and critical shear values were calculated for all treatments using the shear stress model that was originally developed for soil. Rill erodibilities were higher on topsoil-treated plots than on control and compost-treated areas. Yard waste had significantly lower rill erodibility than all other compost and soil treatments. There were no significant differences between critical shear values for the composts and soil. Yard waste compost exhibited greater resistance to rill formation than the biosolids and bio-industrial composts or the two soils. Low R2 values for compost erodibility and critical shear suggest that the shear stress model used in this analysis is not well suited for use with composted organics. Detachment caused by flotation of low-density particles, and bridging caused by coarse particles lodging farther down the slope, are believed to be two rill erosion mechanisms in compost that the shear stress model does not adequately address.