Geology topics

Jillian Lee Hebert

Publications and source records attributed to Jillian Lee Hebert.

2 recordsLinked to original sources

USGS2022

Effects of formaldehyde (Parasite-S®) on biofilter nitrification from a cold- and a warm freshwater RAS

The effect of Parasite-S® (an aqueous formaldehyde solution) on the nitrification processes of biofilters was evaluated in two recirculating aquaculture systems (RASs). Rearing tanks in the warmwater RAS contained yellow perch ( Perca flavescens ) and grass carp ( Ctenopharyngodon idella ) with an initial weight of 166.8 kg and a mean density of 39.5 kg/m 3 . Rearing tanks in the coldwater RAS contained rainbow trout ( Oncorhynchus mykiss ) and lake trout ( Salvelinus namaycush ) with an initial weight of 1377.8 kg at a system density of 41.9 kg/m 3 . Parasite-S® was administered to the entire system on four consecutive days in both trials to achieve a nominal concentration of 14.8 mg/L formaldehyde (40 mg/L formalin) at the biofilter. Removal efficiencies for total ammonia nitrogen (TAN) and nitrite nitrogen were measured as indicators of biofilter nitrification processes. The active ingredient in Parasite-S®, formaldehyde, was measured until it was below the method detection limit of 0.8 mg/L. TAN volumetric removal rate was significantly decreased in both systems after formaldehyde addition and remained below pre-exposure efficiency in the coldwater RAS. Nitrite nitrogen volumetric removal rate was not significantly different, but the slope and intercepts were less after formaldehyde addition indicating an effect on the nitrifying bacteria. Although removal rates were decreased, no mortality occurred after four consecutive formaldehyde indefinite bath exposures in either system.

Aquaculture Research↗

USGS2022

FishStan: Hierarchical Bayesian models for fisheries

Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above example also demonstrates how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and people seeking to use them often must program their own implementations. Furthermore, many fisheries managers and researchers may not have Bayesian programming skills, but many can use interactive languages such as R. Additionally, programs such as JAGS often require long run times (e.g., hours if not days) to fit hierarchical models and programs such as Stan can be more difficult to program because it is a compiled language. We created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.

Journal of Open Source Software↗