Transition probabilities help identify putative drivers of community change in complex systems

Area of Science:

• Ecology
• Marine Biology
• Community Ecology

Background:

• Understanding large-scale ecological processes is vital for predicting climate change impacts and managing ecosystems.
• Species interactions are complex and dynamic, making comprehensive study challenging.
• Current research often focuses on limited interactions, potentially overlooking broader ecological drivers.

Purpose of the Study:

• To quantitatively identify ecological forces regulating communities without oversimplifying interactions.
• To apply multispecies Markov chain models to estimate transition probabilities in the Gulf of Maine intertidal zone.
• To investigate regional differences in community assembly pathways and their drivers.

Main Methods:

• Employed multispecies Markov chain models to estimate transition probabilities between species.
• Quantified faunal element transitions bimonthly over ~2.5 years at three replicate sites in the Gulf of Maine.
• Analyzed regional variations in transitions related to mortality, colonization, and replacement by key species (mussels, barnacles, algae).

Main Results:

• Transition probabilities for key intertidal species differed significantly across the Gulf of Maine.
• Observed regional variations suggest that consumer pressure and recruitment pathways shift with changing community structure.
• Identified specific ecological pathways driving regional community shifts in the Gulf of Maine.

Conclusions:

• Regional shifts in local species interactions can drive large-scale community changes.
• Incorporating interaction complexity is crucial for ecological hypothesis building, experimentation, and forecasting.
• This approach provides a framework for understanding how regional processes influence local community dynamics.