Improving mid-range theory for conservation science

Conservation science sits between rich but often abstract grand theory and a vast body of practice-based case studies, yet it lacks the mid-range theory needed to link the two. This deficit limits our ability to advance knowledge, generalise across cases, and anticipate when and why particular interventions will work, fail, or backfire.

We propose a workflow for building mid-range theory for conservation science using simple quantitative models grounded in a shared ontology of social–ecological systems, and illustrate it based on case studies. Using the Coupled Infrastructure Systems (CIS) Framework, we first delimit the focal conservation system and identify key feedbacks between ecological, social, institutional, and built infrastructures. We then: (1) derive causal loop diagrams focused on specific management-relevant feedbacks; (2) translate these into clearly defined variables and mathematical relationships; (3) connect them into simple simulation models; and (4) use experiments to refine hypotheses, clarify domains of applicability, and identify critical variables and indicators.

We illustrate this process with two conservation problems: human–wildlife conflict around protected areas, and the provision of biodiversity as a public good on privately owned forests under European Union policy. In both cases, the workflow sharpened definitions (e.g. of system boundaries and actors), revealed under-recognised social variables (such as tolerance for wildlife and social license), and clarified where existing grand theories are either too abstract or too narrow to guide decisions. Focusing on feedbacks rather than whole systems reduced complexity while preserving core dynamics, and the shared CIS-based ontology facilitated comparison across cases and the construction of archetypes.

We argue that such mid-range models can improve the parsimony, empirical tractability, and predictive potential of conservation theory, support comparative and archetype-based research, and provide a basis for testing conservation measures before costly implementation. Developing mid-range theory in this way is essential if conservation science is to move beyond repeated trial-and-error learning toward more cumulative, theory-informed action.