Applied Ecosystem Services, LLC

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.

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 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.

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.

Water quality matters for humans, livestock, fish and wildlife, and plants including food crops. Too often policies and regulations are ineffective while restoration projects fail to achieve intended goals. The problem is seen in environmental impact assessments, point- and nonpoint-source discharges, and Superfund sites. While some reasons for failure are project-specific, three common and easily avoided reasons are the lack of knowledge about spatial and temporal distribution of the chemical of concern, no information about the causes and amount of variability, and the focus on concentrations at a local point rather than on the entire ecosystem.

Have you missed a permit compliance monitoring or reporting event and been financially penalized? Has your environmental impact statement approval been delayed by regulators’ paralysis by analysis or by many challenges from project opponents? Has your farm or livestock operation been accused of degrading a nearby water body although you comply with discharge permit monitoring requirements? Have you suffered from the “battle of competing experts” in litigation confusing finders of fact on what your environmental data reveal about the case?

For enforcement of the Endangered Species Act federal regulators (the Fish & Wildlife Service and NOAA Fisheries/National Marine Fisheries Service) consider local populations and management units (stocks) to be species, regardless of biological theory. NOAA Fisheries calls these small groups “Evolutionarily Significant Units” (ESU); the US Fish & Wildlife Service calls them “Discrete Population Segments” (DPS). These political definitions affect how decisions are made regarding ESA-listed biota in the areas where businesses have their operations.

The EPA requires states to protect designated beneficial uses of water such as municipal water supplies; protection of fish, shellfish, and wildlife; and recreational, agricultural, industrial, and navigational purposes. States are required to examine the suitability of a water body for designated uses based on physical, chemical, and biological characteristics as well as its geographical setting, scenic qualities, and economic considerations. EPA’s highest designated use is “fishable/swimmable”. All designated uses are to be assessed to determine whether they do, or can, attain suitable quality.

Regulatory implementation of the Clean Water Act sets quality standards as maximum concentration limits (MCL) of individual elements. Applied to all single-element constituents such values are mis-leading. Toxic metals (arsenic, lead, mercury) are of particular concern yet concentrations of the isolated element do not reflect the various compounds in which these metals are found in rocks, soils, surface waters, or ground waters. More importantly, such elemental concentrations do not reflect bioavailability or ecotoxicity of multi-element chemical compounds.

Water quality discharge permits require periodic measurement of water quality constituent concentrations to document compliance with permit conditions. In addition to correctly including concentrations below the analytical method’s detection level when describing the distribution of these concentrations there is great value for operators and environmental regulators in properly analyzing the temporal aspects of these data. A single concentration, particularly when it exceeds a standard’s threshold, lacks context and does not assess an operation’s interactions with the natural environment; it is a temporal and spatial snapshot.