Brian Klinkenberg

Professor

Research Classification

Geomatics
Biogeography
Bioinformatics
Environmental Health

Research Interests

Geographic Information Science
Environmental Remote Sensing
Spatial Analysis
Volunteered Geographic Information (VGI) and Citizen Science Initiatives

Relevant Degree Programs

 

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Master's students
Any time / year round

Please view my home page for a listing of current projects.

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).

Great Supervisor Week Mentions

Each year graduate students are encouraged to give kudos to their supervisors through social media and our website as part of #GreatSupervisorWeek. Below are students who mentioned this supervisor since the initiative was started in 2017.

 

Brian Klinkenberg gives all of his time and attention to his students. He has never given me the impression that he doesn't want to help, whether it's advice for a presentation, edits for a paper, or a meeting to move a group project forward. Even on the busiest days, he balances working with his graduate students, teaching courses, attending meetings, and still makes time to guide his undergrads. And bonus: he always brings in cookies!

Emily Acheson (2018)

 

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2019)
Modeling place attachment using GIS (2017)

The PlaceInGIS project is a comprehensive examination of how places can be represented using modern Geographic Information System (GIS). After decades of research, geographers now understand that places are dynamic features, whose fuzzy boundaries change over time, subject to internal and external forces. The long-term goal of the PlaceInGIS project is to make people's understanding of place visible, comparable and amenable to analysis.Place attachment is a theoretical construct that permits the quantification, visualization and analysis of the importance of place. The method described makes use of two significant sub-components of place attachment, place dependence and place identity, to create fuzzy surfaces in a GIS.After conducting a detailed GPS mapping exercise of the Colliery Dam Park study area in Nanaimo, British Columbia, Canada, 302 study participants were presented with a survey questionnaire between 2011 and 2012. The place attachment and place dependence components for each feature described were used to create "feature surfaces." These were then combined using a Fuzzy OR operator to generate a single "place attachment surface" for each individual, which can be compared against each other or summed to show the overall opinions of groups.In the short term, we are developing an application called the Place Analysis System (PAS), which enables places to be adequately represented. There are numerous applications for the PAS, as it creates a foundation for the comparative study of place. For the first time, it is possible to visualize, take measurements and analyze place attachment. What was once an ephemeral concept has been made concrete and amenable to study. The PAS can analyze fuzzy boundaries, or the fuzzy boundaries can be defuzzified to be more compatible with traditional representations of data in a GIS. We examine two applications of the PAS, one as a tool for site planning, and the other for the geographical analysis of core and periphery. These applications demonstrate the utility of the PAS, and we conclude by considering further applications and modifications to make the method easier to employ in future studies.

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Geospatial analysis of African elephant movement (Loxodonta africana and L. cyclotis) (2015)

African elephants (Loxodonta africana and L. cyclotis) are important species for geospatial study given their ecological role as megaherbivores, their large home ranges which pose challenges for conservation, and the ongoing ivory crisis. Using GPS tracking data, I address five research topics that contribute new information to the geospatial analysis of tracking data, to elephant movement ecology, and conservation :1. What is an appropriate method to collect, store, disseminate, visualize and analyze elephant tracking data? I present a system (Loxobase) designed to provide an efficient and scientific basis for the treatment of wildlife tracking data. I demonstrate its utility by analyzing tracking datasets collected from 247 elephants (Chapter 2). 2. Can we leverage real-time tracking data for management and conservation? I present a monitoring system that implements continuous analysis of elephant GPS tracking data streams to identify positional and movement-based geospatial alert conditions. Four algorithms identify when wildlife slow or stop moving or cross into or near to spatial objects (Chapter 3).3. Can we estimate wildlife space-use from tracking data? I develop the Elliptical Time-Density model to estimate an animal's utilization distribution from tracking data where parameters are directly linked to species biology. I demonstrate its performance in relation to other space-use estimators (Chapter 4).4. What does tracking data tell us about the movement patterns of the Sahelian elephants in Mali? I use GPS tracking to study elephants in the Gourma, Mali to understand this unique and important population. The Gourma elephant's range was found to exceed those reported elsewhere in Africa and movements were correlated with patterns of rainfall and vegetation phenology. I also identified corridors and core areas of conservation priority (Chapter 5).5. What does tracking data tell us about the factors influencing elephant range size across Africa? I present a comparative analysis of elephant range area measured in West, Central, East and Southern Africa. Using mixed effects models, I test hypotheses about elephant range size in relation to sex, species, region, vegetation phenology and quantity, protected areas, human footprint and terrain (Chapter 6).

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Master's Student Supervision (2010 - 2018)
Citizen science in conservation biology : best practices in the geoweb era (2013)

Conservation biology emerged as an activist discipline in the 1980s in response to increasing evidence that Earth is undergoing a biodiversity crisis. Building on foundations of biological science and applied resource management methods, this new discipline called upon its practitioners to both undertake scientific research to improve understanding of all species and ecosystems, and to take social and political action to protect and enhance endangered biodiversity. In the current era of declining budgets for biodiversity research and management, volunteer citizen science is gaining recognition as an important strategy for expanding and extending the work of embattled professional conservation biologists. New technologies such as handheld computers, GPS, GIS, interactive map services, and the internet, and the wide-spread availability, adoption and adaptation of these technologies by the general public, have created an environment where citizens can be rapidly mobilized to gather, process, and communicate data in support of conservation biology’s twin goals. In this thesis I explore citizen science within conservation biology and within the concept of the GeoWeb. I trace the history of citizen science in biology since the late 1800s to the current day, to better understand the practice and its contribution to conservation science. I find that citizen science is often employed to undertake research at large spatial scales, and that often location is a key attribute of the data citizens gather; as a result, the infrastructure and methods of the GeoWeb are fundamental to many citizen science projects. In the spirit of conservation biology, I pair my research of citizen science with the assembly of a set of best practices for increasing the impact of the practice on the conservation agenda, and then evaluate twelve current citizen science projects currently underway in British Columbia against these practices. I conclude that citizen participation in biological science furthers both of conservation biology’s goals: it both increases our body of knowledge about biodiversity, and helps to develop an informed and empowered constituency for conservation action and ecologically sustainable stewardship.

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A landscape level analysis of yellow-cedar decline in coastal British Columbia (2010)

Yellow-cedar (Chamaecyparis nootkatensis) is currently undergoing a dramatic decline in western North America, with concentrated areas of decline located in southeast Alaska and coastal British Columbia. Recent research suggests that a shift in climate is responsible for the decline and a working hypothesis concerning the role of climate and site specific factors has been proposed. The main objective of this research was to contribute to the understanding of the yellow-cedar decline phenomenon by examining the spatial pattern of the decline and assessing the relations with topographic variables in coastal British Columbia.The research questions were addressed through a combination of remote sensing and Geographic Information System (GIS) techniques. Sample points were distributed across the landscape according to a stratified sampling scheme and the presence/absence of decline at each point was determined using a forest cover dataset and aerial photograph interpretation. Spatial patterns of topographic factors (e.g. elevation, slope, aspect) were derived from a 25 m digital elevation model of the province. To assess the strength of relations between the distribution of decline and the various environmental predictors, logistic regression and decision-tree models were applied. The lasso technique was used to select a significant set of coefficients and the selection was then validated through bootstrap analysis. Model results indicated that low elevation sites close to the coast, which are more exposed and have more variation in elevation, are more likely to show evidence of decline. The logistic model fit the data well (Nagelkerke R² = 0.846, Hosmer-Le Cessie omnibus test failed to find any evidence of lack of fit) and had high predictive accuracy (AUC = 0.98).The topographic variables identified by the model influence degree of soil saturation, temperatures and snowpack presence in a forest stand, supporting the proposed associations in the current decline hypothesis. The analysis also highlighted the utility of the lasso logistic model for selecting significant variables and mapping high risk areas for decline. Knowledge of the determinants of the spatial pattern of decline will improve predictability and provide critical information for the conservation and management of yellow-cedar.

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