Analyzing large-scale conservation interventions with Bayesian hierarchical models: a case study of supplementing threatened Pacific salmon.

Myriad human activities increasingly threaten the existence of many species. A variety of conservation interventions such as habitat restoration, protected areas, and captive breeding have been used to prevent extinctions. Evaluating the effectiveness of these interventions requires appropriate statistical methods, given the quantity and quality of available data. Historically, analysis of variance has been used with some form of predetermined before-after control-impact design to estimate the effects of large-scale experiments or conservation interventions. However, ad hoc retrospective study designs or the presence of random effects at multiple scales may preclude the use of these tools. We evaluated the effects of a large-scale supplementation program on the density of adult Chinook salmon Oncorhynchus tshawytscha from the Snake River basin in the northwestern United States currently listed under the U.S. Endangered Species Act. We analyzed 43 years of data from 22 populations, accounting for random effects across time and space using a form of Bayesian hierarchical time-series model common in analyses of financial markets. We found that varying degrees of supplementation over a period of 25 years increased the density of natural-origin adults, on average, by 0-8% relative to nonsupplementation years. Thirty-nine of the 43 year effects were at least two times larger in magnitude than the mean supplementation effect, suggesting common environmental variables play a more important role in driving interannual variability in adult density. Additional residual variation in density varied considerably across the region, but there was no systematic difference between supplemented and reference populations. Our results demonstrate the power of hierarchical Bayesian models to detect the diffuse effects of management interventions and to quantitatively describe the variability of intervention success. Nevertheless, our study could not address whether ecological factors (e.g., competition) were more important than genetic considerations (e.g., inbreeding depression) in determining the response to supplementation

Feasibility/development study for the removal of ammonia from wastewater using biologically regenerated clinoptilolite

Ammonium nitrogen may be readily removed from wastewaters by means of an ammonium selective ion exchanger, clinoptilolite. The major cost in this process is the cost of regenerating the exchanger in order that it may be reused. This report presents the results of a study to develop a process in which nitrifying bacteria were employed to regenerate the exchanger. The biological regeneration process is shown feasible in both batch and column studies. The mechanism of regeneration was shown to be ion exchange followed by nitrification of the liberated ammonium. The influence of salt concentration, salt composition, particle size, dissolved oxygen, aeration tank configuration, aeration tank size on the r a t e o f regeneration were identified. The selectivity of clinoptilolite for barium, lead, copper, cadmium and zinc was measured in exchange for sodium. To further identify possible toxic effects of heavy metals toxicity studies were conducted with free and complexed copper.U.S. Department of the InteriorU.S. Geological SurveyOpe

Use of graph theory measures to identify errors in record linkage

Ensuring high linkage quality is important in many record linkage applications. Current methods for ensuring quality are manual and resource intensive. This paper seeks to determine the effectiveness of graph theory techniques in identifying record linkage errors. A range of graph theory techniques was applied to two linked datasets, with known truth sets. The ability of graph theory techniques to identify groups containing errors was compared to a widely used threshold setting technique. This methodology shows promise; however, further investigations into graph theory techniques are required. The development of more efficient and effective methods of improving linkage quality will result in higher quality datasets that can be delivered to researchers in shorter timeframes

Cross-border hospital use: analysis using data linkage across four Australian states

Objective: To determine the quality and effectiveness of national data linkage capacity by performing a proof-of-concept project investigating cross-border hospital use and hospital-related deaths. Design, participants and setting: Analysis of person-level linked hospital separation and death registration data of all public and private hospital patients in New South Wales, Queensland and Western Australia and of public hospital patients in South Australia, totalling 7.7 million hospital patients from 1 July 2004 to 30 June 2009. Main outcome measures: Counts and proportions of hospital stays and patient movement patterns. Results: 223 262 patients (3.0%) travelled across a state border to attend hospitals, in particular, far northern and western NSW patients travelling to Queensland and SA hospitals, respectively. A further 48 575 patients (0.6%) moved their place of residence interstate between hospital visits, particularly to and from areas associated with major mining and tourism industries. Over 11 000 cross-border hospital transfers were also identified. Of patients who travelled across a state border to hospital, 2800 (1.3%) died in that hospital. An additional 496 deaths recorded in one jurisdiction occurred within 30 days of hospital separation from another jurisdiction. Conclusions: Access to person-level data linked across jurisdictions identified geographical hot spots of cross-border hospital use and hospitalrelated deaths in Australia. This has implications for planning of health service delivery and for longitudinal follow-up studies, particularly those involving mobile populations

Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)

The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch’s multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11–57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers, and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014–15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines, and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability

Magnetically responsive membranes

The invention provides permeable magnetically responsive filtration membranes that include a filtration membrane polymer base suitable for fluid filtration; hydrophilic polymers conjugated to the surface of the filtration membrane polymer; and magnetic nanoparticles affixed to the ends of a plurality of the hydrophilic polymers, wherein the hydrophilic polymers are movable with respect to the surface of the filtration membrane polymer surface in the presence of an oscillating magnetic field

Quantifying Inter- and Intra-Population Niche Variability Using Hierarchical Bayesian Stable Isotope Mixing Models

Variability in resource use defines the width of a trophic niche occupied by a population. Intra-population variability in resource use may occur across hierarchical levels of population structure from individuals to subpopulations. Understanding how levels of population organization contribute to population niche width is critical to ecology and evolution. Here we describe a hierarchical stable isotope mixing model that can simultaneously estimate both the prey composition of a consumer diet and the diet variability among individuals and across levels of population organization. By explicitly estimating variance components for multiple scales, the model can deconstruct the niche width of a consumer population into relevant levels of population structure. We apply this new approach to stable isotope data from a population of gray wolves from coastal British Columbia, and show support for extensive intra-population niche variability among individuals, social groups, and geographically isolated subpopulations. The analytic method we describe improves mixing models by accounting for diet variability, and improves isotope niche width analysis by quantitatively assessing the contribution of levels of organization to the niche width of a population