Liliana Perez

Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales

Extensive outbreaks of tree-killing insects have been occurring in many parts of North America, including the province of British Columbia, raising concerns about the health of pine forest ecosystems. The dynamic phenomenon of mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, infestation outbreaks is an inherent spatial and temporal complex process. Agent-based modeling (ABM) facilitates simulating spatial interactions that describe the ecological context in which insect populations spread. The main objective of this study was to develop a model of the MPB forest infestation dynamics. This spatially explicit model integrates geographic information systems (GISs) and ABM to simulate MPB outbreaks at the tree and landscape scales, providing spatiotemporal information of annual distribution and patterns of MPB outbreaks. This prototype was implemented with geographic data generated from aerial overview surveys carried out by the B.C. Ministry of Forests and Range, for the study site in Kamloops, Canada. Results show the direct influence that vigorous forest stands and trees have on higher breeding rates, and therefore in the MPB population increment at a tree scale, in a period of 5 years. The simulation results at the landscape level help to determine the most probable locations of future MPB infestations in a time frame of 10 years.

Characterization of spatial relationships between three remotely sensed indirect indicators of biodiversity and climate: a 21years' data series review across the Canadian boreal forest

ABSTRACT Climate drives ecosystem processes and impacts biodiversity. Biodiversity patterns over large areas, such as Canadas boreal, can be monitored using indirect indicators derived from remotely sensed imagery. In this paper, we characterized the historical space–time relationships between climate and a suite of indirect indicators of biodiversity, known as the Dynamic Habitat Index (DHI) to identify where climate variability is co-occurring with changes in biodiversity indicators. We represented biodiversity using three indirect indicators generated from 1987 to 2007 National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer images. By quantifying and clustering temporal variability in climate data, we defined eight homogeneous climate variability zones, where we then analyzed the DHI. Results identified unique areas of change in climate, such as the Hudson Plains, that explain significant variations in DHI. Past variability in temperatures and growing season index had a strong influence on observed vegetation productivity and seasonality changes throughout Canadas boreal. Variation in precipitation, for most of the area, was not associated with DHI changes. The methodology presented here enables assessment of spatial–temporal relationships between biodiversity and climate variability and characterizes distinctive zones of variation that may be used for prioritization and planning to ensure long-term biodiversity conservation in Canada.

Model testing and assessment: Perspectives from a swarm intelligence, agent-based model of forest insect infestations

Abstract Model testing procedures represent a major challenge in the development of agent-based models (ABMs). However, they are required stages for a model to be accepted and to serve as a forecasting, management or decision-making tool. This study presents a comprehensive approach for testing ForestSimMPB, an agent-based model (ABM) designed to simulate mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, outbreaks at the scale of individual trees. ForestSimMPB is a complex system model that is using swarming intelligence, capable to represent individuals’ behaviours and spatial interactions that influence their surrounding environment. Swarm Intelligence (SI) methods are integrated into the ABM in order to reproduce the collective reasoning and indirect communication of autonomous agents representing MPB behaviour within the forest environment. Model testing approach consist of verification, calibration, sensitivity analysis, validation and qualification stages. Model testing is accomplished by simulating MPB infestations using both the ForestSimMPB model and a Random–ABM model that serves as a null model. Outcomes comparison and assessment are performed using raster-based techniques as well as spatial metrics. Aerial photographs of the British Columbia, Canada study sites are used in this model testing approach. Results indicate that ForestSimMPB model representations of MPB outbreaks are more similar than Random model representations to the spatial distribution of MPB-dead trees.

BorealFireSim: A GIS-based cellular automata model of wildfires for the boreal forest of Quebec in a climate change paradigm

Abstract Wildfires are the main cause of forest disturbance in the boreal forest of Canada. Climate change studies forecast important changes in fire cycles, such as increases in fire intensity, severity, and occurrence. The geographical information system (GIS) based cellular automata model, BorealFireSim, serves as a tool to identify future fire patterns in the boreal forest of Quebec, Canada. The model was calibrated using 1950–2010 climate data for the present baseline and forecasts of burning probability up to 2100 were calculated using two RCP scenarios of climate change. Results show that, with every scenario, the mean area burned will likely increase on a provincial scale, while some areas might expect decreases with a low emission scenario. Comparison with other models shows that areas forecasted to have an increase in fire likelihood, overlap with predicted areas of higher vegetation productivity. The results presented in this research aid identifying key areas for fire-dependent species in the near future.

An Agent-Based Model to Identify Migration Pathways of Refugees: The Case of Syria

The Syrian civil war has generated a refugee crisis in the Middle East and Europe. This study draws on complex systems theory and the agent-based modelling method to simulate the movement of refugees in order to identify pathways of forced migration under the present crisis. The model generates refugees as agents and lets them leave conflict areas for a destination that they choose based on their respective characteristics and desires. The simulation outputs are compared with existing data regarding the state of forced migrations of Syrians to assess the performance of the model.

Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change

Adaptation to climate change requires prediction of its impacts, especially on ecosystems. In this work we simulated the change in bird species richness in the boreal forest of Quebec, Canada, under climate change scenarios. To do so, we first analyzed which geographical and bioclimatic variables were the strongest predictors for the spatial distribution of the current resident bird species. Based on canonical redundancy analysis and analysis of variance, we found that annual temperature range, average temperature of the cold season, seasonality of precipitation, precipitation in the wettest season, elevation, and local percentage of wet area had the strongest influence on the species’ distributions. We used these variables with Random Forests, Multivariate Adaptive Regression Splines and Maximum Entropy models to explain spatial variations in species abundance. Future species distributions were calculated by replacing present climatic variables with projections under different climate change pathways. Subsequently, maps of species richness change were produced. The results showed a northward expansion of areas of highest species richness towards the center of the province. Species are also likely to appear near James Bay and Ungava Bay, where rapid climate change is expected.