Applied Ecosystem Services, LLC

The Environmental Issues Doctor

  1. Photo of Environmental Decision-Making in Uncertain Times

    Environmental Decision-making


    Estimated reading time: 4 minutes

    Every business complying with environmental laws can be profitable and sustainable while operating environmentally responsibly. Environmental aspects may not have the same importance to you as other business aspects, but it’s under your control to avoid issues that can cost time and money better spent elsewhere. You should take action now to limit your risk of costly or damaging environmental issues such as permit compliance enforcement. Acting now is especially important because the future is uncertain and the present is constantly changing.
  2. Environmental decision-making for regulators

    The current paradigm used by all federal agencies when preparing NEPA documents is descriptive. It is a qualitative assessment with a decision made subjectively. There is no standardized process used to determine what components are included in the assessment. Scoping too often is separated from public participation. Descriptions of the existing environments are described in words with large technical appendices filled with tables of numbers and graphics. However, there is no attempt to explain what this description means.
  3. Environmental regulations 1

    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 the Clean Water Act, Endangered Species Act, and National Environmental Policy Act were created, and federal 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.
  4. Environmental regulations 2

    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.
  5. Environmental regulations 3

    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.
  6. Environmental regulations 4

    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.
  7. Environmental regulatory science

    Mention regulatory science and the response is always interesting. Many think the term is an oxymoron or a myth, similar to giant shrimp or Sasquatch. Yet robust science applicable to environmental regulations and their enforcement does exist even if not explicit in laws, statutes, or regulations. Most regulatory enforcement actions and almost all challenges by NGOs opposing an operation are based on environmental data, not the language in laws, statutes, or regulations.
  8. Environmental vs laboratory science

    R.A. Fisher, a British biologist and statistician created the statistical foundation for testing experimental hypotheses in the 1930s. Environmental data are observational measurements, not experimental measurements. Therefore, the analytical models applied to experimental data produce incorrect results when applied to environmental data. Download the PDF.
  9. ESA species

    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.
  10. Fisheries enhancement

    Dredging sands and gravels from river beds and scalping annual sediment deposits from bars are too often considered environmentally harmful to aquatic life and water quality by environmental policy makers, regulators, and the public. One reason for this belief is that natural ecosystems are very complex and highly variable. Adding to this complexity and variability altered weather patterns (precipitation and the entire hydrologic cycle) contribute to changed behaviors by fish within each river system.

Providing essential environmental services since 1993.