Suitability Analysis of Acoustic Refugia for Endangered Killer Whales (Orcinus orca) Using the GIS-based Logic Scoring of Preference Method.

An emerging priority in marine noise pollution research is identifying marine "acoustic refugia" where noise levels are relatively low and good-quality habitat is available to acoustically sensitive species. The endangered Southern Resident population of killer whales (Orcinus orca) that inhabits the transboundary Salish Sea in Canada and the USA are affected by noise pollution. Geographic Information Systems (GIS) and spatial multicriteria evaluation (MCE) methods have been used to operationalize suitability analysis in ecology and conservation for site selection problems. However, commonly used methods lack the ability to represent complex logical relationships between input criteria. Therefore, the objective of this study is to apply a more advanced MCE method, known as Logic Scoring of Preference (LSP), to identify acoustic refugia for killer whales in the Salish Sea. This GIS-based LSP-MCE approach considers multiple input criteria by combining input data representing killer whale habitat requirements with noise pollution and other factors to identify suitable acoustic refugia. The results indicate the locations of suitable acoustic refugia and how they are affected by noise pollution from marine vessels in three scenarios developed to represent different levels of vessel traffic. Identifying acoustic refugia can contribute to efforts to reduce the effect of marine noise pollution on killer whale populations by highlighting high-priority areas in which to implement policies such as traffic-limiting measures or marine protected areas. Moreover, the proposed LSP-MCE procedure combines criteria in a stepwise manner that can support environmental management decision-making processes and can be applied to other marine suitability analysis contexts.

A geospatial agent-based model of the spatial urban dynamics of immigrant population: A study of the island of Montreal, Canada.

Residential segregation into spatial neighborhoods and boroughs is a well-known spatial dynamic process that characterise complex urban environments. Existing models of segregation, including the pioneering Schelling ones, often do not consider all the factors that can contribute to this process. Segregation as well as aggregation emerges from local interactions among individuals, and is rooted in the complexity of social, economic and environmental interactions. The main objective of this study is to develop and implement a geospatial agent-based model to simulate the decision-making process of location of new household for incoming immigrant populations. Particularly this study aims to simulate and analyse the dynamics of the new immigrant populations arriving in the bilingual cities and boroughs of the island of Montreal. The model was implemented in NetLogo software, using real geospatial datasets. The obtained simulation results indicate realistic spatial patterns of spatial composition of the ethnographic fabric on the island of Montreal. The proposed model has the potential to be used as part of the city planning purposes.

An agent-based approach for modeling dynamics of contagious disease spread.

BACKGROUND: The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. OBJECTIVE: The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. METHODS: The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. RESULTS: The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. CONCLUSION: The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak.