When are bacteria really gazelles? Comparing patchy ecologies with dimensionless numbers

From micro to planetary scales, spatial heterogeneity - patchiness - is ubiquitous in ecological systems, defining the environments in which organisms move and interact. While this fact has been recognized for decades, most large-scale ecosystem models still use spatially averaged "mean fields" to represent natural populations, while fine-scale, spatially explicit models are mostly restricted to particular organisms or systems. In a conceptual paper, Grunbaum (2012, Interface Focus 2: 150-155) introduced a heuristic framework, based on three dimensionless ratios quantifying movement, reproduction, and resource consumption, to characterize patchy ecological interactions and identify when mean-field assumptions are justifiable. In this paper, we calculated Grunbaum's dimensionless numbers for 33 real interactions between consumers and their resource patches in terrestrial, aquatic, and aerial environments. Consumers ranged in size from bacteria to blue whales, and patches lasted from minutes to millennia, spanning spatial scales of mm to hundreds of km. We found that none of the interactions could be accurately represented by a purely mean-field model, though 26 of them (79%) could be partially simplified by averaging out movement, reproductive, or consumption dynamics. Clustering consumer-resource pairs by their non-dimensional ratios revealed several unexpected dynamic similarities between disparate interactions. For example, bacterial Pseudoalteromonas exploit nutrient plumes in a similar manner to Mongolian gazelles grazing on ephemeral patches of steppe vegetation. Our findings suggest that dimensional analysis is a valuable tool for characterizing ecological patchiness, and can link the dynamics of widely different systems into a single quantitative framework.

SPARSEMODr: Rapid simulations of spatially explicit and stochastic models of infectious disease

1. Simulating the dynamics of realistically complex models of infectious disease is conceptually challenging and computationally expensive. This results in a heavy reliance on customized software and, correspondingly, lower reproducibility across disease modeling studies. 2. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics. The goal of our project, encapsulated by the SPARSEMODr R package, is to offer a framework for rapidly simulating the dynamics of stochastic and spatially-explicit models of infectious disease for use in pedagogical and applied contexts. 3. We outline the universal functions of our package that allow for user-customization while demonstrating the common work flow. 4. SPARSEMODr offers an extendable framework that should allow the open-source community of disease modelers to add new model types and functionalities in future releases.

The epidemicity index of recurrent SARS-CoV-2 infections

Several indices can predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers (e.g. $${{\mathcal{R}}}_{0}$$ R 0 ). Other indices evaluate the potential for transient increases of epidemics eventually doomed to disappearance, based on generalized reactivity analysis. They identify conditions for perturbations to a stable disease-free equilibrium ($${{\mathcal{R}}}_{0}\,<\,1$$ R 0 < 1 ) to grow, possibly causing significant damage. Here, we introduce the epidemicity index e0, a threshold-type indicator: if e0 > 0, initial foci may cause infection peaks even if $${{\mathcal{R}}}_{0}\,<\,1$$ R 0 < 1 . Therefore, effective containment measures should achieve a negative epidemicity index. We use spatially explicit models to rank containment measures for projected evolutions of the ongoing pandemic in Italy. There, we show that, while the effective reproduction number was below one for a sizable timespan, epidemicity remained positive, allowing recurrent infection flare-ups well before the major epidemic rebounding observed in the fall.

The preservation potential of terrestrial biogeographic patterns

Extinction events in the geological past are similar to the present-day biodiversity crisis in that they have a pronounced biogeography, producing dramatic changes in the spatial distributions of species. Reconstructing palaeobiogeographic patterns from fossils therefore allows us to examine the long-term processes governing the formation of regional biotas, and potentially helps build spatially explicit models for future biodiversity loss. However, the extent to which biogeographic patterns can be preserved in the fossil record is not well understood. Here, we perform a suite of simulations based on the present-day distribution of North American mammals, aimed at quantifying the preservation potential of beta diversity and spatial richness patterns over extinction events of varying intensities, and after applying a stepped series of taphonomic filters. We show that taphonomic biases related to body size are the biggest barrier to reconstructing biogeographic patterns over extinction events, but that these may be compensated for by both the small mammal record preserved in bird castings, as well as range expansion in surviving species. Overall, our results suggest that the preservation potential of biogeographic patterns is surprisingly high, and thus that the fossil record represents an invaluable dataset recording the changing spatial distribution of biota over key intervals in Earth History.

The 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19

In response to the COVID-19 pandemic, a number of spatially-explicit models have been developed to better explain the pathways of the disease, to predict the trajectory of the disease, and to test the effect of different health guidelines and policies on the number of cases and deaths. The 1st ACM SIGSPATIAL International Workshop on Modeling and Understanding the Spread of COVID-19 workshop (COVID'2020) featured research efforts that aim to understand the spatial processes and patterns of COVID-19 spread using a variety of spatial modeling, simulation, and mining approaches. The goal of this workshop was to bring together a range of interdisciplinary researchers in the SIGSPATIAL community in the fields of computer science, spatial modeling, social sciences, and epidemiology. Also, this workshop was advertised for anyone interested in infectious disease data and modelling, including but not limited to COVID-19.

Population modelling including spatially explicit models.

R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. R can be used for some quite fast modelling jobs but its speed is nowhere near that of a compiled programming language such as C++. This chapter shows how user-defined functions can be used to perform highly repetitive jobs efficiently, and demonstrates various mathematical functions. The first example shows how a vector can be incremented and the calculated points plotted on a graph as the simulation proceeds. The second example runs a loop, and each time passes values to a user-defined function, and receives back multiple values from that function, which it then stores for plotting later. The third example is necessarily more complex and shows how R code can be used to carry out spatially explicit analyses. Finally, a simple example shows how R can be used to teach how evolution takes place, even in the absence of natural selection due to genetic drift and population bottle-necking.

Population modelling including spatially explicit models.

R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. R can be used for some quite fast modelling jobs but its speed is nowhere near that of a compiled programming language such as C++. This chapter shows how user-defined functions can be used to perform highly repetitive jobs efficiently, and demonstrates various mathematical functions. The first example shows how a vector can be incremented and the calculated points plotted on a graph as the simulation proceeds. The second example runs a loop, and each time passes values to a user-defined function, and receives back multiple values from that function, which it then stores for plotting later. The third example is necessarily more complex and shows how R code can be used to carry out spatially explicit analyses. Finally, a simple example shows how R can be used to teach how evolution takes place, even in the absence of natural selection due to genetic drift and population bottle-necking.