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In advancing the understanding of ecological systems, tools and methodologies borrowed from physics offer powerful interpretative framework to unravel the complexity of the interactions between species and their environment. While such methodologies often face the challenge to reproduce the granular details of ecosystems, they generate insight on a more general level into the temporal dynamics of -and energy flows through- ecological networks and into the response of ecosystems to perturbations.
The aim of this interdisciplinary Collection is to advance the understanding of the temporal dynamics of ecological networks. Amidst the insight such temporal dynamics can generate, we focus our lens on the ecosystem response to perturbing events such as habitat loss, species invasions, climate change, and anthropogenic impacts. The phenomenology of interest include, but is not limited to: cascading of events into broader-scale population dynamics, energy flows through networks, resilience and stability of ecosystems, pattern formations and connectivity in ecological networks, energy flows through networks, and critical transitions within ecological systems.
We invite researchers to contribute their original research articles to this cross-journal collection born from the collaboration between Communications Physics and Communications Earth & Environment and Scientific Reports. We welcome methodological advances able to treat the complexity of ecological networks, as well as application of already established methods based on simulated and field data able to generate new insight in the dynamics and response of such systems.
This Collection supports and amplifies research related to SDG 15.
Species forming complex ecological or economic ecosystems are organized in hierarchies and the ranks of such species are determined by the adjacency matrix of their interaction network. We introduce a framework to calculate the ranks of species by finding the optimal permutation of rows and columns that makes the adjacency matrix maximally nested.
In evolutionary theory, Fisher’s fundamental theorem of natural selection establishes a simple relation between the variance of the growth rate and the temporal increase in the average growth rate. Here, the authors extend the theorem based on statistical physics and information theory and show that the speed in dynamical systems describing nonlinear population dynamics is bounded by Fisher information with universal limit exponents only depending on the kind of bifurcation and not on the specific systems.