Thu, Aug 18, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/Methods: Understanding ecological dynamics has been a central topic in ecology since its origins. Yet, identifying dynamic regimes remains a research frontier for modern ecology. The concept of ecological dynamic regimes emerged to stress the dynamic property of steady states in nature, referring to the fluctuations of ecosystems around some trend or average. Despite methodological developments in theoretical ecology, the implementation of this concept in empirical science is still challenging given the high dimensionality and stochasticity of the data and the large volume of data required to distinguish between stochastic and general, predictable dynamics. The era of big data and the investment in long-term monitoring networks bring new opportunities to study dynamic regimes in empirical ecology. In our work, we propose a novel methodological framework to describe ecological dynamic regimes in a multidimensional state space from ecological trajectories. Our framework includes a formal definition of ecological dynamic regimes, their identification from empirical data, and several analyses and metrics to characterize and compare different dynamic regimes. We used artificial data to illustrate our framework, and we applied our analyses to real data, using permanent sampling plots of boreal forests in Quebec (Canada) as an example.
Results/Conclusions: Our results highlight the importance of considering multidimensional approaches and demonstrate the value of our framework to describe ecological dynamic regimes from large volumes of data compiled in long-term permanent plots. In our study case, we showed that the proposed framework enables identifying, characterizing, and comparing ecological dynamic regimes quantitatively, even when asynchronous, uncomplete trajectories are only available. Our framework allows addressing multiple research questions, including the study of succession in long-lived ecosystems –where it can be used as a robust alternative to space-for-time substitution approaches –, the variability of ecological properties over time, and the responses of populations, communities, and ecosystems to the threats associated with global change.
Results/Conclusions: Our results highlight the importance of considering multidimensional approaches and demonstrate the value of our framework to describe ecological dynamic regimes from large volumes of data compiled in long-term permanent plots. In our study case, we showed that the proposed framework enables identifying, characterizing, and comparing ecological dynamic regimes quantitatively, even when asynchronous, uncomplete trajectories are only available. Our framework allows addressing multiple research questions, including the study of succession in long-lived ecosystems –where it can be used as a robust alternative to space-for-time substitution approaches –, the variability of ecological properties over time, and the responses of populations, communities, and ecosystems to the threats associated with global change.