Conventional framing

Biological causation is often described in terms of interactions occurring at specific “levels of organisation,” such as molecular, cellular, organismal, or ecological. Explanations frequently privilege one level—most commonly the molecular or genetic—as the primary site of causation, with higher-level phenomena treated as derivative or emergent.

This approach can be useful for analysis, but it risks fragmenting biological explanation by treating causation as localised within discrete layers of organisation.

APS reframing

APS rejects the idea that causation is confined to or originates from discrete levels. Instead, it understands causation as inherently multi-scale: a property of systems in which processes are dynamically coupled across spatial and temporal extent.

Biological organisation is continuous and integrated. Processes at different scales—molecular regulation, cellular dynamics, organismal activity, and ecological interaction—are not independent layers but interacting aspects of a single viability-oriented system.

Causation therefore operates through:

  • Reciprocity — processes at different scales influence one another
  • Coupling — interactions are structured through constraint-closed organisation
  • Propagation — effects spread across scales through coordinated activity

No single scale has intrinsic causal priority. What appears as “bottom-up” or “top-down” causation reflects different perspectives on the same underlying organisation.

Multi-scale causation is thus not an addition to mechanistic explanation but a clarification of how causation operates in living systems: as distributed, integrated, and continuously coordinated across scales.

In brief

Multi-scale causation describes how causal influence in biological systems arises from the coordinated interaction of processes across scales. APS treats causation as scale-coupled and reciprocal, rather than localised within hierarchical levels.

Key Point

Causation in biology is not level-bound but scale-coupled—emerging from the reciprocal interaction of processes within a unified, viability-oriented organisation.