Estimating abundance without recaptures of marked pallid sturgeon in the Mississippi River

Abundance estimates are essential for assessing the viability of populations and the risks posed by alternative management actions. An effort to estimate abundance via a repeated mark‐recapture experiment may fail to recapture marked individuals. We devised a method for obtaining lower bounds on abundance in the absence of recaptures for both panmictic and spatially structured populations. The method assumes few enough recaptures were expected to be missed by random chance. The upper Bayesian credible limit on expected recaptures allows probabilistic statements about the minimum number of individuals present in the population. We applied this method to data from a 12‐year survey of pallid sturgeon (Scaphirhynchus albus) in the lower and middle Mississippi River (U.S.A.). None of the 241 individuals marked was recaptured in the survey. After accounting for survival and movement, our model‐averaged estimate of the total abundance of pallid sturgeon ≥3 years old in the study area had a 1%, 5%, or 25% chance of being <4,600, 7,000, or 15,000, respectively. When we assumed fish were distributed in proportion to survey catch per unit effort, the farthest downstream reach in the survey hosted at least 4.5–15 fish per river kilometer (rkm), whereas the remainder of the reaches in the lower and middle Mississippi River hosted at least 2.6–8.5 fish/rkm for all model variations examined. The lower Mississippi River had an average density of pallid sturgeon ≥3 years old of at least 3.0–9.8 fish/rkm. The choice of Bayesian prior was the largest source of uncertainty we considered but did not alter the order of magnitude of lower bounds. Nil‐recapture estimates of abundance are highly uncertain and require careful communication but can deliver insights from experiments that might otherwise be considered a failure.     

Investing in rangeland restoration in the Arid West,USA: Countering the effects of an invasive weed on the long-term fire cycle

In large areas of the arid western United States, much of which are federally managed, fire frequencies and associated management costs are escalating as flammable, invasive cheatgrass (Bromus tectorum) increases its stronghold. Cheatgrass invasion and the subsequent increase in fire frequency result in the loss of native vegetation, less predictable forage availability for livestock and wildlife, and increased costs and risk associated with firefighting. Revegetation following fire on land that is partially invaded by cheatgrass can reduce both the dominance of cheatgrass and its associated high fire rate. Thus restoration can be viewed as an investment in fire-prevention and, if native seed is used, an investment in maintaining native vegetation on the landscape. Here we develop and employ a Markov model of vegetation dynamics for the sagebrush steppe ecosystem to predict vegetation change and management costs under different intensities and types of post-fire revegetation. We use the results to estimate the minimum total cost curves for maintaining native vegetation on the landscape and for preventing cheatgrass dominance. Our results show that across a variety of model parameter possibilities, increased investment in post-fire revegetation reduces long-term fire management costs by more than enough to offset the costs of revegetation. These results support that a policy of intensive post-fire revegetation will reduce long-term management costs for this ecosystem, in addition to providing environmental benefits. This information may help justify costs associated with revegetation and raise the priority of restoration in federal land budgets.     

Implications of simultaneity in a physical damage function

A modeler must often rely on highly simplified representations of complex physical systems when analyzing associated economic issues. Herein, we consider a management problem in which a bioeconomic system exhibits simultaneity in processes governing productivity and damage. In this case, it may benefit the producer to sacrifice productivity to reduce the costs associated with increased damage. We specify empirically a structural damage relationship that explains the biological process by which an invasive species damages a host and estimate the structural model and its reduced form with an exceptional dataset on infestation of olives by the olive fruit fly. We contrast the results of these models with the approach typically taken in the economic literature, which expresses damage as a function of pest density. The population-based approach introduces significantly greater bias into the individual grower's choice of damage-control inputs than estimates based on the structural model.     

Description and evaluation of a model of deposition velocities for routine estimates of dry deposition over North America. Part II: review of past measurements and model results

Numerical sensitivity tests and four months of complete model runs have been conducted for the Routine Deposition Model (RDM). The influence of individual model inputs on dry deposition velocity as a function of land-use category (LUC) and pollutant (SO₂, O₃, SO²⁻₄ and HNO₃) were examined over a realistic range of values for solar radiation, stability and wind speed. Spatial and temporal variations in RDM deposition velocity (V_d) during June – September 1996 time period generated using meteorological input from a mesoscale model run at 35 km resolution over north-eastern North America were also examined. Comparison of RDM V_d values to a variety of measurements of dry deposition velocities of SO₂, O₃, SO²⁻₄ and NHO₃ that have been reported in the literature demonstrated that RDM produces realistic results. Over northeastern NA RDM monthly averaged dry deposition velocities for SO₂ vary from 0.2 to 3.0 cm s⁻¹ with the highest deposition velocities over water surfaces. For O₃, the monthly averaged dry deposition velocities are from 0.05 to 1.0 cm s⁻¹ with the lowest values over water surfaces and the highest over forested areas. For HNO₃, the monthly averaged dry deposition velocities have the range of 0.5 to 6 cm s⁻¹, with the highest values for forested areas. For SO²⁻₄, they range from 0.05–1.5 cm s⁻¹, with the lowest values over water and the highest over forest. The monthly averaged dry deposition velocities for SO₂ and O₃ are higher in the growing season compared to the fall, but this behaviour is not apparent for HNO₃ and sulphate. In the daytime, the hourly averaged dry deposition velocities for SO₂, O₃, SO²⁻₄ and HNO₃ are higher than that in the nighttime over most of the vegetated area. The diurnal variation is most evident for surfaces with large values for leaf area index (LAI), such as forests. Based on the results presented in this paper, it is concluded that RDM V_d values can be combined with measured air concentrations over hourly, daily or weekly periods to determine dry deposition amounts and with wet deposition measurements to provide seasonal estimates of total deposition and estimates of the relative importance of dry deposition.     

Bacterial community profiles from sediments of the Anacostia River using metabolic and molecular analyses

Background aim and scope  

Though the tidal Anacostia River, a highly polluted riverine system, has been well characterized with regard to contaminants, its overall resident bacterial populations have remained largely unknown. Improving the health of this system will rely upon enhanced understanding of the diversity and functions of these communities. Bacterial DNA was extracted from archived (AR, year 2000) and fresh sediments (RE, year 2006) collected from various locations within the Anacostia River. Using a combination of metabolic and molecular techniques, community snapshots of sediment bacterial diversity and activity were produced.     

Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.