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There are many plant and animal species considered to be threatened, endangered, or of special concern to regulators and the public. Correctly estimating population sizes, relationship to habitats, and potential effects of industrial activities is crucial to many permit issues and lawsuits. Other environmental issues are more broad, such as quantifying relationships of industrial activities and natural ecosystems.
The most effective approach to addressing these issues is to quantify causality (cause-and-effect) and explain it in language understood by non-technical decision-makers or finders of fact. Many environmental regulatory concerns and lawsuits involve concerns about “healthy ecosystems” or “sustainable populations.” These are subjective concepts and cannot be directly measured, but are at the core of environmental permitting decisions by regulators and the public and are often the basis for legal challenges.
Abstract concepts cannot be directly measured. Social sciences (e.g., psychology, economics, sociology) are based on abstract concepts like these and decades ago their practitioners adopted suitable statistical models to analyze them and apply results to real issues.
As an example, it is important to objectively define a sustainable population of Greater sage grouse; natural resource operators, the public, and environmental NGOs all need to know.
Potential causal variables might include habitat quantity, quality, and distribution, predation (nest/egg, juvenile, adult), fecundity (fertility of the population), demographics (births minus deaths), and anthropogenic activities. These are measurable indicators hypothesized to contribute to sustainability. They may not all be independent (a prerequisite of frequentist statistical hypothesis testing models) so the analysis must include correlations. For example, habitat quality and distribution might be correlated and habitat quantity and nest predation might be correlated.
There may also be many unobserved (latent) indicators that contribute to sustainability. Latent variables include measurement errors (did the surveyors actually see all the birds that were present during the site visit?), and factors we did not include because we were not aware of them. These causal and response factors are drawn as a map with connections between them. This map (or directed graph) reflects the paths we hypothesize that explain the data we observe in our surveys of sage-grouse, their habitats, and potential predators.
This work was originally published on the Applied Ecosystem Services, LLC web site at https://www.appl-ecosys.com/blog/environmental-issues-involving-fish-and-wildlife/
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.
