Applied Ecosystem Services, LLC

To make informed regulatory decisions it is necessary to understand differences between ecological and environmental data. Analyses of environmental data historically use models developed by numerical ecologists for ecological data collected by academic and research agency scientists. These numeric and statistic models require well-structured data collected to fit assumptions and requirements of the models. This works for researchers who identify a question to be answered and work forward from that to determine when, where, and how much data need collecting to answer that question.

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.

Toxic metals and organics commonly occur in very low concentrations in water, sediments, soils, and rocks. These concentrations are so low they cannot be quantified by analytical chemists and today’s instruments. Censored data are commonly mis-analyzed with potential costly, unnecessary, or harmful results. EPA regulations and guidelines often tell data analysts to ignore (drop) censored data or substitute an arbitrary value. The results of dropping or substituting arbitrary values are wrong.

Across the western US drought, wildland fires, cheatgrass, Western juniper, Lahontan cutthroat trout, bull trout, salmon, bald eagles, desert tortoise, and sage grouse all affect where and how natural resource companies operate. Project planning and approvals can be greatly facilitated by application of advanced statistical and spatial models to environmental data. Causal relationships between explanatory variables such as habitat, food, and predators to response variables (species numbers and distributions) may be explained by linear regression models.

Natural resource companies do not object to environmental regulations that are consistent and support predictability. Consistency and predictability are critical for decision making under conditions of uncertainty. Natural ecosystems are inherently variable across a broad range of temporal and spatial scales; climate change, drought, and societal desires for sustainability make people more aware of this variability. The science used for development and enforcement of environmental regulations has not kept pace with developments in ecological theory and the analytical tools capable of describing, characterizing, classifying, and predicting natural ecosystems as well as distinguishing natural variability from anthropogenic changes.

Natural resource companies do not object to environmental regulations that are consistent and support predictability. Consistency and predictability are critical for decision making under conditions of uncertainty. Natural ecosystems are inherently variable across a broad range of temporal and spatial scales; climate change, drought, and societal desires for sustainability make people more aware of this variability. The science used for development and enforcement of environmental regulations has not kept pace with developments in ecological theory and the analytical tools capable of describing, characterizing, classifying, and predicting natural ecosystems as well as distinguishing natural variability from anthropogenic changes.

Everyone recognizes that environmental data have spatial attributes. We are all familiar with GIS-produced maps showing where data were collected in relation to each other and to other variables. However, maps do not provide a critical requirement to objectively make operational decisions, justify environmental policies and regulations, or help finders of fact in litigation reach sound decisions. What maps lack is the quantitative description of patterns and relationships that reflect underlying environmental processes.

There are many animal species whose population numbers bring them to the attention of resource agencies and others; e.g., Greater sage-grouse, Oregon spotted frog, Lahontan cutthroat trout. Some of these species are listed under the ESA, others are not; in both cases accurate estimates of population size and limiting factors are critical for informed policy and management decisions. Correctly measuring population size and the factors affecting it is not always obvious because of the data formats and mathematical formulation of the statistical models.

Environmental chemistry data are expensive to obtain and valuable and need proper care in storage so they retain their value and return your investment in them. Expenses start with permit application preparation and baseline collections and continue through monitoring programs, analyses, and reporting. The proper storage of environmental data is in an appropriately designed database, but many organizations use spreadsheets instead because they are readily available and easy for individuals to learn and use.