Feedback loops drive ecological succession: towards a unified conceptual framework

Area of Science:

• Ecology
• Plant Community Dynamics
• Ecological Succession

Background:

• Ecological succession is primarily driven by plant-environment feedback loops, where community regrowth alters the environment, influencing species composition.
• Existing models often focus on specific feedback loops, but succession is frequently observed to be highly variable.
• Variability stems from stochastic processes (e.g., mortality, dispersal) and extrinsic factors not influenced by the plant community.

Purpose of the Study:

• To present a new conceptual framework for ecological succession that integrates various driving forces and sources of variability.
• To provide a systematic approach for identifying feedback processes and variation causes across successional stages.
• To guide comparisons among study sites, conceptualization of research, and design of field studies.

Main Methods:

• Development of a conceptual framework defining seven general causes of succession (landscape context, disturbance, biotic/abiotic factors, species availability/performance, plant community).
• Distinguishing between causes that drive succession via feedback loops and those that create variability as extrinsic factors.
• Application of the framework to an extensive field study to compare empirical outcomes with conceptual models.

Main Results:

• The framework systematically identifies feedback processes and variation causes in ecological succession.
• Mapping a field study revealed discrepancies between empirical results and existing conceptual models, highlighting alternative causal pathways.
• Predictability and variability are intrinsically linked features of ecological succession, influenced by both internal feedback and external factors.

Conclusions:

• The proposed framework offers a unified approach to understanding the complex dynamics of ecological succession.
• There is a need to move beyond dominant conceptual models and explore interactions among alternative causal pathways.
• Integrating long-term studies, controlled experiments, and modeling across gradients is crucial for advancing succession research.