Applied Ecosystem Services, LLC

The EPA, and many state regulators, consider aquatic life to be the highest designated beneficial use of water. Closely related to this water quality standard is “fishable and swimmable”. The latter is easier to define and to assess attainment: if fish are present all water quality variables suit their needs; when there are no human parasites or known toxic chemicals water quality is swimmable. The aquatic life water quality standard is not as easy to define and measure.

Every business complying with environmental laws can be profitable and sustainable while operating responsibly. Learning how to go beyond minimal permit compliance requirements to avoid regulatory compliance enforcement actions is as important as are other aspects of your job or operations when you are responsible for environmental permits. The rapidly warming climate and resulting more frequent and severe weather patterns such as megadroughts, massive wildland fire, severe flooding, hotter summers, and colder winters affect environmental permit holders in unpredictable ways.

We live in a time of rapid changes and uncertainties in our climate, health, and economy. The “new normal” is not likely to stabilize for at least another year. The western US is entering the third decade of a megadrought that Columbia University’s Lamont Geological Observatory considers to be the worst in 1,200 years. The megadrought affects the area bounded approximately by the Columbia River on the north, northern Mexico to the south, the Rocky Mountains to the east and the Pacific Ocean on the west.

The Clean Water Act’s (CWA) Section 301(m)(2) reads: “The effluent limitations established under a permit issued under paragraph (1) shall be sufficient to implement the applicable State water quality standards, to assure the protection of public water supplies and protection and propagation of a balanced, indigenous population of shellfish, fish, fauna, wildlife, and other aquatic organisms, and to allow recreational activities in and on the water. In setting such limitations, the Administrator shall take into account any seasonal variations and the need for an adequate margin of safety, considering the lack of essential knowledge concerning the relationship between effluent limitations and water quality and the lack of essential knowledge of the effects of discharges on beneficial uses of the receiving waters.

After 50 years it is time to bring environmental policy and regulatory decision making into the 21st century by applying statistical paradigms that produce technically sound and legally defensible results from environmental data. When federal environmental laws were created, and agencies directed to develop regulations to ensure compliance with them, biologists and ecologists knew less about environmental systems and data analyses than we do today. Scientists had insufficient data for the wide variety of ecosystems covered by these laws, and the only statistical paradigm they knew was the null hypothesis/significance testing (NHST) approach.

The null hypothesis/significance testing (NHST) analytical paradigm does not produce answers for environmental 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 paradigm overcomes many of NHST’s problems and can be applied to environmental data when its limitations are understood. The NHST approach tests how well the data fit a single null hypothesis. The Maximum Likelihood Estimation (MLE) approach tests how well multiple hypotheses fit the data and identifies the hypothesis that maximizes the likelihood of explaining the data.

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. The Clean Water, Endangered Species, and National Environmental Policy Acts are three statutes affecting natural resource industries.

From baseline conditions for environmental impact assessments to compliance with regulatory permit conditions regulated companies collect biological data and report analytical results to regulators and other interested parties. Historically, analyses used biotic diversity and integrity indices. These attempt to summarize highly complex natural ecosystems in a single number believed to make comparisons and decisions easier. While these indices are based on ecological theory they are very difficult, even impossible, to measure and quantitatively compare.

Toxic metals and organic compounds are commonly present at very low concentrations in water, sediments, soils, and rocks. Concentrations cannot be quantified with 99% certainty; if those chemicals are present the instrument cannot distinguish them from zero. Concentrations below laboratory reporting limits are censored because their values are unknown. Censored values can be 70-80% of the available date, a meaningful amount of valuable information. Correct analysis of censored data is particularly important when performing an ecological risk analysis (ERA) as part of the CERCLA Superfund process.