Applied Ecosystem Services, LLC

The Environmental Issues Doctor

Photo of Profitting From Your Environmental Data


Estimated reading time: 2 minutes

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. These analyses use mean environmental variable values which might yield misleading relationships and result in poor decisions.

Statistical distributions of environmental data often have unequal variation because of complex interactions between the explanatory factors and the biota. Yet all of these variables can be measured or included in the appropriate statistical models. Unequal variation means more than one slope (rate of change) describes the relationship between a response variable and explanatory variables measured on a subset of these factors. Quantile regression estimates multiple rates of change (slopes) from the minimum to maximum response. This produces a more complete picture of the relationships between explanatory and response variables than do other regression models. The ecological concept of limiting factors constraining species’ populations and distribution often focuses on rates of change in quantiles near the maximum response when only a subset of limiting factors are measured. Complex causation analyses can be quantified using path analysis and semantic equation modeling.

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 Reference Areas

    Reference Areas


    Estimated reading time: 2 minutes

    Natural ecosystems are complex and highly variable at multiple size scales. Because of the difficulties of accurately summarizing complexity and variability in an index number, regulators often require a reference area for comparison with a proposed or reclaimed project area. Agreement on a suitable reference area may be a requirement prior to permitting or bond-release decisions for mining and logging operations. It is common for selection of an acceptable reference area to take a long time.
  2. Photo of Regulatory Data Models

    Regulatory Data Models


    Estimated reading time: 2 minutes

    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.

Providing essential environmental services since 1993.