A validated agent-based model to study the spatial and temporal heterogeneities of malaria incidence in the rainforest environment

BACKGROUND: The Amazon environment has been exposed in the last decades to radical changes that have been accompanied by a remarkable rise of both Plasmodium falciparum and Plasmodium vivax malaria. The malaria transmission process is highly influenced by factors such as spatial and temporal heterogeneities of the environment and individual-based characteristics of mosquitoes and humans populations. All these determinant factors can be simulated effectively trough agent-based models. METHODS: This paper presents a validated agent-based model of local-scale malaria transmission. The model reproduces the environment of a typical riverine village in the northern Peruvian Amazon, where the malaria transmission is highly seasonal and apparently associated with flooding of large areas caused by the neighbouring river. Agents representing humans, mosquitoes and the two species of Plasmodium (P.falciparum and P. vivax) are simulated in a spatially explicit representation of the environment around the village. The model environment includes: climate, people houses positions and elevation. A representation of changes in the mosquito breeding areas extension caused by the river flooding is also included in the simulation environment. RESULTS: A calibration process was carried out to reproduce the variations of the malaria monthly incidence over a period of 3 years. The calibrated model is also able to reproduce the spatial heterogeneities of local scale malaria transmission. A “what if” eradication strategy scenario is proposed: if the mosquito breeding sites are eliminated through mosquito larva habitat management in a buffer area extended at least 200 m around the village, the malaria transmission is eradicated from the village. CONCLUSIONS: The use of agent-based models can reproduce effectively the spatiotemporal variations of the malaria transmission in a low endemicity environment dominated by river floodings like in the Amazon

Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict

The Mass Elevation Effect of the Central Andes and Its Implications for the Southern Hemisphere's Highest Treeline

One of the highest treelines in the world is at 4810 m above sea level on the Sajama Volcano in the central Andes. The climatological cause of that exceptionally high treeline position is still unclear. Although it has been suggested that the mass elevation effect (MEE) explains the upward shift of treelines in the Altiplano region, the magnitude of MEE has not yet been quantified for that region. This paper defines MEE as the air temperature difference in summer at the same elevation between the inner mountains/plateaus (Altiplano) and the free atmosphere above the adjacent lowlands of the Andean Cordillera. The Altiplano air temperature was obtained from the Global Historical Climatology Network-Monthly temperature database, and the air temperature above the adjacent lowlands was interpolated based on the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis 1 data set. We analyzed the mean air temperature differences for January, July, and the warm months from October to April. The air temperature was mostly higher on the Altiplano than over the neighboring lowlands at the same altitude. The air temperature difference increased from the outer Andean east-facing slope to the interior of the Altiplano in summer, and it increased from high latitudes to low latitudes in winter. The mean air temperature in the Altiplano in summer is approximately 5 K higher than it is above the adjacent lowlands at the same mean elevation, averaging about 3700 m above sea level. This upward shift of isotherms in the inner part of the Cordillera enables the treeline to climb to 4810 m, with shrub-size trees reaching even higher. Therefore, the MEE explains the occurrence of one of the world’s highest treelines in the central Andes