Environmental Systems Science

Foundation for Environmental Science

Introduction to Environmental Systems Science

Environmental Systems Science was born out of necessity, not in a university, but in a courtroom. When the DDT War began in 1966, it quickly became clear that traditional scientific testimony was inadequate. Isolated data points could not explain the cumulative, long-term effects of DDT and its principal environmental metabolite, DDE, on non-target organisms. A new framework was needed that could describe the transport, transformation, and impact of toxic substances across an entire interconnected environmental system.

That framework first emerged publicly in the spring of 1969 during the final phase of the DDT hearings in Madison, Wisconsin. There, scientists and their attorney synthesized thousands of pages of technical data into a coherent analysis of how DDT behaved within the Lake Michigan regional ecosystem, a system that encompassed not only forests, rivers, and lakes, but also human infrastructure, social behavior, and public health. This systems-based perspective allowed the trial judge and the political decision-makers to understand environmental risk not in isolation, but as a function of multiple interdependent variables.

The concept was later published formally in the prestigious scientific journal Science, and presented to the academic community at the annual meeting of the American Association for the Advancement of Science (AAAS) in Boston. It marked the beginning of a new approach to environmental science rooted in systems thinking, conceptual modeling, and a long-term ecological perspective.

Environmental Systems Science enables us to see the environment as a complex, interrelated system; to investigate and analyze the dynamic relationships that drive its behavior; and to develop models that reflect its real-world function. It rejects the academic fiction that the environment can be understood in fragments aligned with the narrow boundaries of university departments. From the moment it was introduced in 1969, Environmental Systems Science exposed the feudal structure of higher education, where each department was a fortified castle, guarding its territory and blind to everything beyond its walls. Environmental Systems Science is a scientific discipline born in a courtroom, where outcomes matter and truth cannot hide behind jargon. It has no ideology but the scientific method described by Sir Francis Bacon in 1620. It evolved from necessity. It now demands recognition.

Ecology

Ecology is often characterized as the scientific study of the “web of life.” Human beings can be found somewhere in that web, either as spinner or unwilling captive, and there has been much written about man’s “place in nature.” As a modern science, Ecology deals with organisms in their environment and with the processes that link organisms and their habitats.

Ecology, however, is more than the study of any organism in its environment; it is the integrated study of organisms and their environment. Ecology involves consideration of the prerequisites for human existence on earth: the essential physical and chemical factors, food, and energy. Ecology as an integrative discipline provides a framework within which seemingly disparate human activities can be seen in relationship to each other.

The study of the relationships among different organisms and between organisms and their environment has resulted in the description of various biogeochemical cycles. The most nearly perfect cyclical processes are those involving water and nitrogen, while the least cyclical processes are those in which material is removed from the continents and deposited in the permanent basins of the ocean.

In 1969, Paul Shepard observed that:
“Ecology . . . as such cannot be studied, only organisms, earth, air, and sea can be studied. [Ecology] is not a discipline: there is no body of thought and technique which frames an ecology of man. It must be therefore a scope or a way of seeing. Such a perspective on the human situation is very old and has been part of philosophy and art for thousands of years. It badly needs attention and revival.”
The Subversive Science: Essays Toward An Ecology of Man (1969).

The fundamental and basic concepts of animal ecology are also the fundamental and basic concepts of human ecology. The laws of nature apply to the human species as they do to animals. Human beings cannot ignore the dynamic forces of the environment with impunity.

Ecosystems

A natural community is an assemblage of populations of plants, animals, bacteria, fungi and other microorganisms that live together in some place at some time and interact with one another and their environment to such an extent that they may be considered together as a system of some definite composition and structure. A community and its associated environment, when considered as a functional system of complementary relationships — with transfer and circulation of matter and energy — is called an ecosystem.

Ecosystems are real — like a pond, a forest, or an ocean — but they are also abstractions: conceptual schemes developed from the study of real systems. The primary ecological processes of energy flow and material cycling are foundational to ecosystem dynamics.

Ecological Systems

Ecological systems consist of many components that interact in a variety of ways. Each biological component is affected by abiotic factors, and system variables change with time and space. These interactions are complex, often nonlinear, and best understood through systems analysis.

Ecological systems cannot be fully understood through isolated parts. The art of systems ecology is to identify the governing components and relationships that control system behavior. These principles apply not just to natural systems, but to economic, social, and political systems as well.

Ecological Systems

Ecological systems consist of many components that interact in a variety of ways. Each biological component is affected by abiotic factors, and system variables change with time and space. These interactions are complex, often nonlinear, and best understood through systems analysis.

Ecological systems cannot be fully understood through isolated parts. The art of systems ecology is to identify the governing components and relationships that control system behavior. These principles apply not just to natural systems, but to economic, social, and political systems as well.

The Regional Environmental System

Before the Regional Environmental System can be defined, the word “environment” must be clearly understood. It is often used imprecisely, but in environmental systems science, it refers to the full set of natural, social, economic, and societal resources in a region — and the interactions among them. The term gained legal precision in courtroom settings beginning in 1966, including the pivotal DDT litigation.

The constant feedback between human beings and their environment implies a continuous alteration of both. The various aspects of biological and social nature constitute such an integrated system that they can be altered only within a certain range.

A regional environmental system includes aquifers, recharge areas, precipitation, climate, topography, watersheds, groundwater, vegetation, wildlife, scenic and historic features, and the human social, political, and economic elements that interact with them. These interactions define the system — and the region — across space and time.

The Elements of Regional Environmental Systems

Ecosystems, when viewed as functionally integrated communities, are the largest operational units of a regional environmental system. These systems may contain one or more identifiable ecosystems that act as nested or interacting regional ecological systems.

A systems approach is required to identify boundaries and optimize interactions within these nonlinear, dynamic systems. The goal is to describe the real region as it exists — not as an abstract model divorced from reality.

Environmental Systems Analysis

Environmental systems analysis evaluates how any change to one element affects the broader system. It treats environmental relationships as interdependent sets, and models their behavior through time and space. Its roots lie in operations research, probability, computer science, and applied mathematics — but its goal is to support real-world decision-making, especially when data is incomplete.

The core idea is that complex processes can be broken into simpler unit functions. One powerful technique involves recurrence functions: expressing a system’s state at time t+1 as a function of its state at time t. This enables dynamic modeling of cause-effect sequences.

Recurrence Relationships

Recurrence relationships are foundational to systems analysis. From Markov matrices to dynamic programming, recurrence models express the evolution of a system over time by feeding each stage’s output into the next. These relationships can span backward or forward time steps, allowing multi-stage causal modeling in complex systems.

Optimization

Optimization is the application of mathematical techniques to identify the best outcome — typically the maximization or minimization of a system function — subject to real-world constraints. In environmental systems, this includes resource use, emissions, habitat integrity, economic costs, and social equity.

Tools for Environmental Systems Analysis

Environmental systems often require high-dimensional, iterative solution methods such as nonlinear regression or gradient search. Feedback control, inequality constraints, thresholds, loops, and dispersal processes through time and space are all integral. Many ecological cycles naturally lend themselves to cyclic computational structures.

Information in Environmental Systems Science

Information, in systems terms, is a measure of order. The more ordered (or “negentropic”) a system is, the more information it contains. This concept is essential when analyzing how ecosystems organize themselves and how disturbances affect system structure and resilience.

Models

A model is a simplified representation of a system’s components and their relationships, constructed to enable understanding, prediction, and manipulation. In environmental systems, mathematical models often replace physical models due to the complexity of the systems involved. Models allow simulation of cause-effect pathways and forecast outcomes of different interventions.

The Process of Modeling Environmental Systems

Building a model starts with identifying relevant state variables, processes, and relationships. Conceptual models evolve into mathematical models, which are tested by comparing predictions with observed behaviors. The goal is not perfect replication, but useful simulation to support real-world choices.

The Elements of Environmental Systems Models

Environmental systems models often include layered structures to reflect geology, hydrology, soils, vegetation, and land use. Ian McHarg’s Design With Nature pioneered this integrative approach. Each layer contributes to understanding the cumulative and interactive effects of policy or environmental change.

Causal Relationships in Environmental Systems Models

Causal pathways — from geology to stream patterns to vegetation and land use — form the basis for spatial models. By understanding these sequences, planners and analysts can predict outcomes across time and space. Vegetation and animal patterns reflect and respond to these causal systems.

Static and Dynamic Modeling

All models are snapshots — even dynamic ones begin with static representations. However, systems modeling must evolve to reflect change. Geological layers may be static over decades, but soils, vegetation, water flow, and land use shift. A robust model distinguishes what can change from what cannot — and projects possible future trajectories.

Mathematical Models

Mathematical models are abstract representations that require both technical expertise and contextual insight. Their usefulness depends not on completeness, but on relevance, realism, and proper application. They help answer questions of “what if” — but must be carefully matched to the questions being asked.

Testing and Validation of Environmental Systems Models

No model is useful without validation. Testing models against real-world outcomes — while avoiding oversimplification or bias toward convenient techniques — is essential. Models should fit the system, not the method. Political misuse of modeling (e.g., climate simplification or econometric distortion) should be actively resisted.

The Vocabulary of Environmental Systems Science

Key terms include:

• Decision variables – controlled or influenced inputs
• Policy – a set of assigned values for decision variables
• Feasible policy – a policy that obeys system constraints
• Policy space – the total set of possible feasible policies
• State variables – current conditions of system elements
• System parameters – factors outside current system control
• State vectors – directional variables tracking time, space, or both
• Objective function – criteria for evaluating policy outcomes
• Constraints – natural, social, or technical limits on the system

The Objective Function in Environmental Systems Science

Objective functions link policies to outcomes. In environmental systems, they may include commensurate (quantifiable) and non-commensurate (qualitative or political) values. The formulation of the objective function — and the constraints it must obey — is one of the most critical steps in any model’s construction.

Environmental Systems Optimization Considerations

No universal optimization method exists. Environmental systems require case-specific approaches, depending on:

• The structure of the model
• Data availability
• Required precision
• Available computing resources

Operations research and control theory offer tools — but must be applied with care. “Programming” here means planning, not coding, and always with real-world relevance in mind.

The Future Path for Environmental Systems Modeling

The path forward lies in integrating fine-scale ecological insights with national-level data (e.g., remote sensing), combining quantitative modeling with scenario testing, and educating decision-makers on systems thinking. Environmental systems science empowers public policy to consider not just immediate consequences, but systemic risks and long-term outcomes.

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