arXiv subjects

J. M. Calabrese

Publications and source records attributed to J. M. Calabrese.

3 recordsLinked to original sources

arXiv2025

Scaling of connectivity metrics in river networks

Rivers exhibit fractal-like properties that are associated with scaling laws linking geometry and size. The optimal channel network (OCN) model, which is a mathematically tractable representation of river networks often used in theoretical studies, is based on the fractal properties of rivers and consequently reproduces geometric scaling laws. However, purely geometric relationships may not fully capture the interaction between river structure and species' movement strategies that is most relevant to many large-scale ecological processes. In contrast, connectivity, which is a concept that blends habitat geometry and individual movement, has been shown both theoretically and empirically to influence relevant large-scale ecological outcomes across a broad array of ecosystems. Here, we analyze networks from more than 1000 major rivers around the world, including the Amazon, Mississippi, and Nile, to investigate how river network connectivity metrics scale with system size. Specifically, we found clear power-law scaling of both the harmonic centrality and betweenness centrality network connectivity metrics. To assess the extent to which OCNs can capture these empirical connectivity patterns, we generated synthetic river networks by fitting an OCN model to each real river. We found excellent agreement between empirical and OCN-based scaling laws, supporting the notion that OCNs can accurately represent rivers in network-based models and analyses. Finally, we examined the robustness of the connectivity scaling laws to species movement strategies ranging from ideal shortest-path navigation to suboptimal random-path navigation. Surprisingly, we found that random navigation breaks the power-law scaling relationship for harmonic centrality, but not for betweenness centrality.

physics.soc-ph↗

arXiv2013

On the estimators of autocorrelation model parameters

Estimation of autocorrelations and spectral densities is of fundamental importance in many fields of science, from identifying pulsar signals in astronomy to measuring heart beats in medicine. In circumstances where one is interested in specific autocorrelation functions that do not fit into any simple families of models, such as auto-regressive moving average (ARMA), estimating model parameters is generally approached in one of two ways: by fitting the model autocorrelation function to a non-parameteric autocorrelation estimate via regression analysis or by fitting the model autocorrelation function directly to the data via maximum likelihood. Prior literature suggests that variogram regression yields parameter estimates of comparable quality to maximum likelihood. In this letter we demonstrate that, as sample size is increases, the accuracy of the maximum-likelihood estimates (MLE) ultimately improves by orders of magnitude beyond that of variogram regression. For relatively continuous and Gaussian processes, this improvement can occur for sample sizes of less than 100. Moreover, even where the accuracy of these methods is comparable, the MLE remains almost universally better and, more critically, variogram regression does not provide reliable confidence intervals. Inaccurate regression parameter estimates are typically accompanied by underestimated standard errors, whereas likelihood provides reliable confidence intervals.

stat.ME↗

arXiv2010

Dynamical phase coexistence: A simple solution to the "savanna problem"

We introduce the concept of 'dynamical phase coexistence' to provide a simple solution for a long-standing problem in theoretical ecology, the so-called "savanna problem". The challenge is to understand why in savanna ecosystems trees and grasses coexist in a robust way with large spatio-temporal variability. We propose a simple model, a variant of the Contact Process (CP), which includes two key extra features: varying external (environmental/rainfall) conditions and tree age. The system fluctuates locally between a woodland and a grassland phase, corresponding to the active and absorbing phases of the underlying pure contact process. This leads to a highly variable stable phase characterized by patches of the woodland and grassland phases coexisting dynamically. We show that the mean time to tree extinction under this model increases as a power-law of system size and can be of the order of 10,000,000 years in even moderately sized savannas. Finally, we demonstrate that while local interactions among trees may influence tree spatial distribution and the order of the transition between woodland and grassland phases, they do not affect dynamical coexistence. We expect dynamical coexistence to be relevant in other contexts in physics, biology or the social sciences.

physics.bio-ph↗