Applied Ecosystem Services, LLC

The Environmental Issues Doctor

Photo of Bringing Environmental Policy and Regulation into the 21st Century, Part 4


Estimated reading time: 3 minutes

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. Section 101(a)(2) of the Clean Water Act (CWA) states, “it is the national goal that wherever attainable, an interim goal of water quality which provides for the protection and propagation of fish, shellfish, and wildlife and provides for recreation in and on the water be achieved …” The best way to assess attainment of this goal is measurement of aquatic ecosystem variables, their statistical analysis, and interpretation of results in appropriate ecological contexts.

The EPA’s approach is “Guidelines for Deriving Numerical National Water Quality Criteria for the Protection Of Aquatic Organisms and Their Uses” that aims to develop “a national criterion … needed for a particular material.”

There are so many differences in aquatic ecosystems within the US that a more pragmatic approach uses the most sensitive aquatic biota – benthic macroinvertebrates – to quantify the local community’s variability and response to anthropogenic activities. Natural variability will change over time, even in areas with no immediate anthropogenic activities. Natural variability establishes a baseline against which anthropogenic operations can be quantitatively compared. The Endangered Species Act (ESA) has laudable goals but the details of implementation continue to be subject to scientific and legal dispute.

Advanced statistical models appropriate to quantify relationships between animal populations, distributions, and habitats have been developed in the past quarter-century. These include quantile regression examining populationhabitat relationships depending on the population size quantiles

species’ presence or absence at a location and estimate the probability distribution of a population given dozens, or hundreds, of potential explanatory variables.

The National Environmental Policy Act (NEPA) publicly discloses the process used by lead agencies when selecting the preferred alternative for a project such as a mine or electrical power generator. NEPA’s Section 101 (a) states that “environmental quality is to be maintained consistent with fulfilling the social, economic, and other requirements of present and future generations of Americans.”

Advanced statistical models quantify existing conditions and cause-andeffect relationships. Ecological interpretation of results provide objective criteria for federal decision-making. These models quantify the dynamics of the existing ecosystems, forecast potential effects of each alternative, and objectively describe future conditions. These situations frequently occur when a species of concern is thought to be present in a project’s area.

Regulators should adopt these procedures because they justify decisions based on project-specific science and data analyses. Operators need to adopt these environmental data analytic models because they quantify environmental stewardship and sustainability and are metrics that can reduce or eliminate environmental risks. Results inform decisions by site managers, corporate executives and Boards, investors, and lenders.

This work was originally published on the Applied Ecosystem Services, LLC web site at

It is offered under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. In short, you may copy and redistribute the material in any medium or format as long as you credit Dr. Richard Shepard as the author. You may not use the material for commercial purposes, and you may not distribute modified versions.

Keep reading

  1. Photo of Analyzing aquatic biological data

    Analyzing aquatic biological data


    Estimated reading time: 2 minutes

    Regulatory and resource agencies sometimes require collection of benthic macroinvertebrates and/or fish for baseline data or permit compliance. Too often they do not know what they will do with the data. Correctly analyzing and interpreting these data yields valuable information that operators and regulators can use to make well-informed decisions regarding Clean Water Act compliance. Aquatic biotic communities reflect ambient water quality conditions much better than do chemical concentrations. This is particularly true for benthic macroinvertebrates as they are less mobile than fish.
  2. Photo of Bringing Environmental Policy and Regulation into the 21st Century, Part 3

    Bringing Environmental Policy and Regulation into the 21st Century, Part 3


    Estimated reading time: 3 minutes

    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.

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