A simulator for spatially extended kappa models

International audienceSpatial Kappa is a simulator of models written in a variant of the rule-based stochastic modelling language Kappa, with spatial extensions. Availability: The spatial kappa simulator is an open-source project licensed under the LGPLv3, with Java source, binaries and manual available a

An efficient approach to noise analysis through multidimensional physics-based models

IEEE TRANS. EL. DEV

Simple individual-based models effectively represent Afrotropical forest bird movement in complex landscapes

Reliable estimates of dispersal rates between habitat patches (i.e. functional connectivity) are critical for predicting long-term effects of habitat fragmentation on population persistence. Connectivity measures are frequently derived from least cost path or graph-based approaches, despite the fact that these methods make biologically unrealistic assumptions. Individual-based models (IBMs) have been proposed as an alternative as they allow modelling movement behaviour in response to landscape resistance. However, IBMs typically require excessive data to be useful for management. Here, we test the extent to which an IBM requiring only an uncomplicated set of movement rules [the 'stochastic movement simulator' (SMS)] can predict animal movement behaviour in real-world landscapes. Movement behaviour of two forest birds, the Cabanis's greenbul Phyllastrephus cabanisi (a forest specialist) and the white-starred robin Pogonocichla stellata (a habitat generalist), across an Afrotropical matrix was simulated using SMS. Predictions from SMS were evaluated against a set of detailed movement paths collected by radiotracking homing individuals. SMS was capable of generating credible predictions of bird movement, although simulations were sensitive to the cost values and the movement rules specified. Model performance was generally highest when movement was simulated across low-contrasting cost surfaces and when virtual individuals were assigned low directional persistence and limited perceptual range. SMS better predicted movements of the habitat specialist than the habitat generalist, which highlights its potential to model functional connectivity when species movements are affected by the matrix. Synthesis and applications. Modelling the dispersal process with greater biological realism is likely to be critical for improving our predictive capability regarding functional connectivity and population persistence. For more realistic models to be widely applied, it is vital that their application is not overly complicated or data demanding. Here, we show that given relatively basic understanding of a species' dispersal ecology, the stochastic movement simulator represents a promising tool for estimating connectivity, which can help improve the design of functional ecological networks aimed at successful species conservation

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Remote sensing applications in forestry - The development of an earth resources information system using aerial photographs and digital computers Annual progress report

Earth resources information system using aerial photographs and digital computer

Random Parameters and Spatial Heterogeneity using Rchoice package in R

This study focus on models with spatially varying coefficients using simulations.  As shown by Sarrias (2019), this modeling strategy is intended to complement the existing approaches by using variables at micro level and by adding flexibility and realism to the potential domain of the coefficient on the geographical space. Spatial heterogeneity is modelled by allowing the parameters associated with each observed variable to vary “randomly” across space according to some distribution. To show the main advantages of this modeling strategy, the Rchoice package in R is used

Coupling remote sensing with wildfire spread modeling in Mediterranean areas

Wildfires are a threat to the ecosystems and in the future this threat could become stronger due to climate change. Spatially explicit fire spread models are effective tools to study fire behavior and wildfire risk. However, to run fire spread simulations, one of the most important inputs is represented by fuel models and this information is not always available. In the last decades, remote sensing technologies have offered valuable information for the classification and characterization of fuels. For this reason, in this work we created accurate maps of main fuel types for Mediterranean areas combining multispectral and LiDAR data. This information improves the current available information, which derives from the Land Use Map of Sardinia. We also enhanced the characterization of canopy fuel models using LiDAR data producing canopy layers ready to be used for wildfire spread modeling. Finally, we compared the variation in simulated wildfire spread and behavior determined by the use of fine-scale maps v. lower resolution maps. In these simulations, we assessed also the effect of using LiDAR-derived canopy layers as well. The results showed more accurate outputs when using our custom fuel and canopy layers produced in this work. In conclusion, this work suggests that the use of LiDAR and satellite imagery data can contribute to improve estimates of modeled wildfire behavior

The Grid Dependence of Well Inflow Performance in Reservoir Simulation

Imperial Users onl