Applied Ecosystem Services, LLC

The null hypothesis/significance testing (NHST or frequentist) analytical paradigm does not produce answers for environmental policy or regulatory decisions because rejecting the null hypothesis (of no difference between data sets) says nothing about why or by how much they differ. The likelihood (information theoretic) paradigm overcomes many of NHST’s problems and can be applied to environmental data when its limitations are understood. Download the PDF.

The frequentist and likelihood frameworks for analyzing environmental data assume that there is a “true” state of the world represented by the values described by a single hypothesis and its probability distribution. The Bayesian framework assumes that observations are the “truth” while the hypotheses explaining the observations have probability distributions. The Bayesian approach solves many conceptual problems of applying the frequentist approach to environmental data because Bayesian results depend on observations (or measurements) rather than on a range of hypothetical outcomes.

The three previous parts of this series described statistical frameworks for objectively analyzing environmental data and explaining where each is appropriate. Correct statistical models applied to environmental concerns are powerful tools for regulators, permit holders, attorneys, and consultants. Results are more technically sound and legally defensible than the commonly used methods. Appropriate statistical analyses can demonstrate compliance with statutory goals and objectives. Download the PDF.

The original form of this article was submitted on March 31, 1995 as the Direct Service Industries’ comments to draft regulations proposed by the U.S. Fish and Wildlife Service (FWS) and the National Marine Fisheries Service (NMFS, now NOAA Fisheries). The two agencies wanted to define “distinct population segments” under the Endangered Species Act (ESA) so that they would have a consistent definition for their regulatory decisions. Unfortunately, administrative convenience and political accommodation replaced science in the definition.

Predicting concentrations of chemicals in surface waters is a major component of permitting decisions, from NEPA impact assessments and NPDES point source discharge to mine closure and Superfund liability bond releases. Decision delays are costly for operators, and regulators are too often sued by those claiming that decisions were based on inadequate data. Usual approaches to forecasting chemical concentrations are to build complex numeric ecosystem models or predict concentrations of single chemicals rather than the entire set of chemicals of interest.

The Resource Conservation and Recovery Act (RCRA) as implemented by EPA and state regulations requires monitoring of ground water chemistry and statistical analyses of these data. The latest revision of the EPA’s statistical guidance document is 887 pages long (plus supplements) and has been augmented by a Webinar because the statistical analyses are not simple or easily understood by non-statisticians/data analysts. Some commercial software is sold to perform these analyses, but like all other statistical software it does not ensure that the user completely understands how to select models to apply or can properly interpret the results.

Dissolved metals such as copper, cadmium, and zinc can be toxic to aquatic life, particularly fish. The current tool used to estimate site-specific water quality criteria for a metal is the biotic ligand model (BLM). The BLM intends to quantify how water chemistry affects speciation and biological availability of metals in aquatic ecosystems. This is important because bioavailability and bioreactivity of metals control their potential for acute or chronic harm. A BLM incorporates aquatic chemistry, fish physiology, and ecotoxicology but not ecology.

NEPA, CEQ regulations, and agency directives describe in detail what is to be done in preparing an EA or EIS that is compliant with the law and all regulations. It does not direct staff or external contractors how each requirement is to be met. This white paper presents specific requirements and explains how the APPLIED ECOSYSTEM SERVICES’ quantitative approximate reasoning model, Eikos™ fulfills these requirements so that the results are demonstrably technically sound and legally defensible.

Nonpoint source water pollution adversely impacting a beneficial use is regulated by discharge allocations from a Total Maximum Daily Load. Whether a waterbody requires a TMDL depends on how its condition is assessed. The Clean Water Act, as amended in 1987, specifies using environmental ambient conditions to assess waterbodies, but regulators focus on single chemical ions or compounds based on point source discharge regulations. The limitations of this approach and the benefits of a different approach are explained here.

Natural ecosystems are complex and highly variable at multiple size scales. Because of the difficulties of accurately summarizing complexity and variability in an index number, regulators often require a reference area for comparison with a proposed or reclaimed project area. Agreement on a suitable reference area may be a requirement prior to permitting or bond-release decisions for mining and logging operations. It is common for selection of an acceptable reference area to take a long time.