When I tell people I studied environmental science and political ecology before becoming a technical writer, I usually get puzzled looks. What does understanding threatened ecosystems have to do with documenting APIs or managing AI-assisted workflows?
The answer is everything. Systems theory—the framework that helped me understand how governance impacts ecological resilience—has become one of the lenses through which I approach software architecture, documentation strategy, and AI integration.
Ecological Resilience vs. Software Reliability
In my early academic work, I focused on how human governance systems impact ecological stability. One core concept was resilience: an ecosystem’s ability to maintain essential functions despite external shocks. A resilient forest survives wildfire by regenerating from deep root systems; a resilient wetland processes pollution spikes without collapsing.
Software systems face analogous challenges. The industry has shifted from prioritizing “robustness” (i.e., building systems that resist failure) to embracing “resilience,” designing systems that fail gracefully and recover quickly.
Consider a monolithic application where a single memory leak brings down the entire system. In ecological terms, this is like an invasive species that monopolizes resources and crashes the entire food web. Just as technology can threaten ecological balance through unintended externalities, a poorly isolated “feature” in a monolithic architecture becomes a pollutant that degrades performance across the entire environment.
The solution in both domains is similar: create buffer zones and compartmentalization. Ecologists design wildlife corridors that contain localized disturbances. Software architects implement circuit breakers and microservices that isolate failures and prevent cascading collapses. When learning about software systems, I look for these isolation patterns as an architectural philosophy that protects system health.
Governance and Technical Debt
My background studying governance systems prepared me for understanding technical debt. In environmental management, rigid or poorly informed policies lead to catastrophic outcomes. For example, irrigation systems that create dead zones, or forestry policies that suppress natural fires until fuel loads become catastrophic.
Software has its own governance: the rules, standards, and conventions dictating how components interact. When this governance is rigid or uninformed, systems become brittle. When the “Expert-in-the-Loop”—the human with contextual understanding—is removed from critical decisions, you get the software equivalent of ecological collapse.
This is why I’m cautious about AI-generated code. An AI might confidently suggest deprecated methods or invent non-existent API parameters. Without adaptive governance involving rigorous verification and comprehensive documentation, these errors propagate like invasive species through an unmonitored ecosystem.
The parallel extends to how both systems accumulate debt. Environmental degradation results from short-term decisions that externalize costs to the future. Technical debt accumulates the same way: quick fixes that seem expedient but create compounding maintenance burdens. In both cases, governance determines whether those debts become manageable or catastrophic.
Documentation as Ecosystem Mapping
When I document software systems, I don’t catalog individual API endpoints in isolation. I map the ecosystem: how data flows, how components depend on each other, what feedback loops exist, where boundaries are defined. This is fundamentally a systems-theory approach to information architecture.
Just as an ecologist must understand why species thrive or decline within their context, I must extract the “why” behind architectural decisions. Why does this service retry failed requests? What upstream conditions make this endpoint vulnerable? How does this component fit into larger workflows users actually care about?
The Expert-in-the-Loop as Environmental Steward
Threats to ecosystems often stem from “blind” automation or inadequate oversight: industrial processes that externalize pollution and algorithms that optimize narrow metrics while degrading broader system health. I apply this cautionary principle to AI-assisted workflows.
The ultimate systemic skill—whether in ecology or software—is discernment. Knowing when to rely on automated efficiency and when human strategic thinking is non-negotiable. Understanding that systems are more than the sum of their parts, and that expertise means seeing the whole while attending to the details.
Systems theory gave me mental models to understand threatened ecosystems. Those same models now help me navigate the complex, interconnected world of modern software development. The vocabulary changes, but the fundamental patterns remain consistent.