It is well known that global climate change causes an increase in forest fire frequency and severity. Thus, understanding fire dynamics is necessary to comprehend the mitigation of the negative effects of forest fires. Our objective was to inform how fire spreads in a simulated two-species forest with varying wind strengths. The forest in this study was comprised of two different tree species with varying probabilities of transferring fire that was randomly distributed in space at densities (C tot ) ranging from 0.0 (low) to 1.0 (high). We studied the distribution pattern of burnt trees by using local rules of the two-dimensional model. This model incorporated wind blowing from south to north with strength (P w ) ranging from 0.0 (low) to 1.0 (high). Simulation results showed that when C tot > 0.45 the fire covered the entire forest, but when C tot 0.45 the fire did not spread. The wind effect on the variation of the amount of the burnt tree was maximized at the critical density and dramatically decreased with increasing C tot . Additionally, we found that the term of C tot and P w plays an important role in determining the distribution.

Subterranean termite nests are located underground and termites forage out by constructing tunnels to reach food resources, and tunneling behavior is critical in order to maximize the foraging efficiency. Excavation, transportation, and deposition behavior are involved in the tunneling, and termites have to move back and forth to do this. Although there are three sequential behaviors, excavation has been the focus of most previous studies. In this study, we investigated the deposition behavior of the Formosan subterranean termite, Coptotermes formosanus Shiraki, in experimental arenas having different widths (2, 3, and 4 mm), and characterized the function of deposited particles. We also simulated moving distance of the termites in different functions. Our results showed that total amounts of deposited particles were significantly higher in broad (4 mm width) than narrow (2 mm) tunnels and most deposited particles were observed near the tip of the tunnel regardless of tunnel widths. In addition, we found that deposited particles followed a quadratic decrease function, and simulation results showed that moving distance of termites in this function was the shortest. The quadratic decrease function of deposited particles in both experiment and simulation suggested that short moving distance in the decrease quadratic function is a strategy to minimize moving distance during the deposition behavior.

Forest fires are expected to increase in severity and frequency under global climate change and thus better understanding of fire dynamics is critical for mitigation and adaptation. Researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed various simulation models to reproduce forest fire spread dynamics. However, these models have limitations in the fire spreading because of the complicated factors such as fuel types, wind, and moisture. In this study, we suggested a simple model considering the wind effect and two different fuel types. The two fuels correspond to susceptible tree and resistant tree with different probabilities of transferring fire. The trees were randomly distributed in simulation space with a density ranging from 0.0 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to quantify how the forest fire patterns are affected by wind and tree density. The statistical analysis showed that the total tree density had greatest effect on the forest fire spreading and wind had the next greatest effect. The density of the susceptible tree was relatively lower factor affecting the forest fire. We believe that our model can be a useful tool to explore forest fire spreading patterns.

It has been known that some termites are responsible for tunnel excavation for foraging, while others are not involved in the excavation. The biological reason for this is that the resting termites are a backup for the termites that have used up their energy in the tunneling activity. In this study, we build an agent‐based model (ABM) wherein agents (simulated termites) follow simple rules that govern their behavior. In this model, the agents are endowed with a directional vector that has been shown to exist in real termites, but they do not communicate through pheromonal or physical marking of excavation sites. They move toward the tunnel tips, tunnel when their progress in that direction is blocked, and transport the excavated soil. Using the model, we investigated the work efficiency of termites in constructing tunnels and transporting food; the efficiency was defined as the inverse value of tunnel connectivity plus tunnel expansion speed. Biologically, the connectivity is related to the energy to be used for termites to transport food through tunnels, and the tunnel expansion speed is related to the energy required for constructing tunnels. Simulation results showed that the efficiency was maximized at an intermediate number of termites. This means that termites were better to be inactive to maintain the high efficiency when too many workers are present in the colony. We briefly discuss the strength and weakness of the ABM and the values of this study in relation to termite foraging strategy.

Understanding the forest fire patterns is necessary to comprehend the stability of the forest ecosystems. Thus, researchers have suggested the simulation models to mimic the forest fire spread dynamics, which enables us to predict the forest damage in the scenarios that are difficult to be experimentally tested in laboratory scale. However, many of the models have the limitation that many of them did not consider the complicated environmental factors, such as fuel types, wind, and moisture. In this study, we suggested a simple model with the factors, especially, the geomorphological structure of the forest and two types of fuel. The two fuels correspond to susceptible tree and resistant tree with different probabilities of transferring fire. The trees were randomly distributed in simulation space at densities ranging from 0.5 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to quantify how the forest fire patterns are affected by the structure and tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.

No abstract

The rise of sea levels due to global warming is a problem of concern at an international scope and the causes are already known relatively clearly. Every year, the Intergovernmental Panel on Climate Change (IPCC) creates a scenario for greenhouse gas emissions and predicts the global average sea-level rise rate accordingly. It is necessary to estimate the rate of sea-level rise to date in creating such a scenario. In particular, since the height of the sea level changes (SLC) continuously, the errors of SLC may occur due to various causes with a fragmental analysis. To estimate the sea-level rise accurately, we applied Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is based on the Empirical Mode Decomposition (EMD) to decompose the tidal level. Through this, we discover that the differences in the local sea-level rise rate occurred even within a small area. To understand each component of tide level decomposed through CEEMDAN, we confirm the component-wise/regional correlation between tidal stations. In addition, we looked at how local sea-level rise correlated with the global meteorological phenomenon, El Niño-Southern Oscillation (ENSO) which is one of the most influential recurring climate patterns Socioeconomically.

Because forest fires are predicted to increase in severity and frequency under global climate change with important environmental implications, an understanding of fire dynamics is critical for mitigation of these negative effects. For the reason, researchers with different background, such as ecologists, physicists, and mathematical biologists, have developed the simulation models to mimic the forest fire spread patterns. In this study, we suggested a novel model considering the wind effect. Our theoretical forest was comprised of two different tree species with varying probabilities of transferring fire that were randomly distributed in space at densities ranging from 0.0 (low) to 1.0 (high). We then studied the distributional patterns of burnt trees using a two-dimensional stochastic cellular automata model with minimized local rules. We investigated the time, T, that the number of burnt trees reaches 25% of the whole trees for different values of the initial tree density, fire transition probability, and the degree of wind strength. Simulation results showed that the values of T decreased with the increase of tree density, and the wind effect decreased in the case of too high or low tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.