We welcome the new addition to the group Greg Rickbeil and Gladys Tecson who were hired in December 2018 and January 2019 respectively. Greg is a postdoc that will be working on Q2.1 and Gladys replaced Curtis Marr as Project Manager.
Congratulations to Ethan Berman who successfully defended his thesis on January 28, 2019 and to Curtis Marr who took up a new position at Simon Fraser University, we wish them both the best in their future endeavors.
Q1.1 Broad Scale Mapping of ecoAnthromes / Road detection
Sean Kearney coordinated with industry partners to collect road condition data using RoadLab Pro app on mobile devices. This data is being combined with data collected by fRI Research and UBC. Preliminary analysis is underway to use these datasets to train and validate automatic road detection and classification using high spatial resolution RapidEye satellite imagery. Sean is doing analysis of data from nighttime lights and/or other population-related data, social media posts, recent disturbances (e.g., logging activity) and anthropogenic features (e.g., well pads, campgrounds), to model the use-intensity of road segments, based on where people are coming from, where people are going and the distance and road surface conditions between start and end locations. He continues work to develop a machine learning algorithm to automatically detect roads from RapidEye satellite imagery. Initial algorithm development is completed and being tested.
Q1.2 Snow-melt Dynamics
Ethan Berman completed data analysis relating spring snow cover to grizzly bear movement and habitat use. He had submitted a research article to a journal for review. He successfully graduated his MSc in January 2019.
Q1.3 Grizzly Bear / Song Bird Surrogacy
Emily Cicon, in her ARU studies, created GLMs bird richness models with explanatory factors, for example distance to specific vegetation types . She also added a multi-variate (meta-MDS) analysis of songbird richness by bear activity and sex of bear and added bear RSF to analysis with GLMM (relationship between RSF and bird spp. richness).
Q2.1 Yellowhead Grizzly Bear Population Demographic Analysis
Greg Rickbeil will continue the work from Sean Coogan (who resigned from Grizzly-PAW on July 2018) as well as working on Q3A.5. Greg has begun coding Brownian Bridge space use models for den sites, building model for predicting state switching due to environmental changes and assessing of daily snow cover versus behavioral state.
Q2.2 Modelling and Simulating Grizzly Bear Food Supply
Chris Souliere finished chapter 1 manuscript on Q2.2 (food supply in fire and harvest) that he will be submitting for publication within the year. He also learnt enough individual-based modeling philosophy and programming in Netlogo to start building IBM model which he aims to obtain preliminary results.
Q3A.1 Bear Movement – Completed
Q3A.2 Forest Structure and Movement
Brandon Prehn travelled to the International Bear Research and Management 2018 conference in Ljublana, Slovenia (September). His presentation was awarded best student presentation in the said conference. Brandon drafted his first manuscript on relationships between bear movement and LIDAR derived vegetation attributes.
Q3A.3 Phenology of Grizzly Bear Foods and their Relationship to Movement
Cam McClelland completed the processing and analysis of the data from the 2018 phenological camera collected from 10 sites and validated the 30m Landsat phenology dataset for the yellowhead region. He began work on writing his first paper.
Q3A.4 Grizzly Bear Movement and Roads
Bethany Arndt had her first committee meeting and finished a 30-page proposal for her project. She wrote and executed code to build viewshed in arcpy and wrote on R code for movement analyses.
Q3A.5 Contextualizing Movement and Roads
Greg Rickbeil has taken on this role as part of his PDF project.
Q3B.1 Physiological Markers
Abbey Wilson has extracted bulk samples for two additional discovery runs to identify detectable proteins. In October, Abby completed all hair hormone analyses (cortisol, testosterone, estradiol, progesterone). She began analyzing data for the first manuscript (in collaboration with Sean Kearney) demonstrating changes in hair cortisol concentration that are related to landscape change across the Yellowhead BMA. She likewise outlined PDF manuscripts and began communication with collaborators.
Q3B.2 Physiology and climate change
This position has been rolled into Q3B.1 due to funding reductions.
Abbey Wilson is a post-doctoral fellow in the Department of Veterinary Biomedical Sciences at the University of Saskatchewan. Abbey grew up in Virginia, USA, but spent her summers in southern California, USA, where she enjoyed riding horses, hiking, and playing on school sports teams. Abbey completed her undergraduate work at Sweet Briar College in Virginia, earning a B.S. in biology. During this time, she rode competitively for the college equestrian team, conducted research on plant and pollinator relationships, and spent a semester abroad in Seville, Spain. Prior to graduate school, Abbey assisted in both large and small animal veterinary practices and completed an internship with the Memphis Zoo Conservation and Research Department. She determined the reproductive status of ocelots and snow leopards by measuring hormone concentrations and assisted staff with reproductive physiology techniques. Abbey recently completed her Ph.D. at Mississippi State University with research focused on identifying pheromone candidates in giant panda urine, secretions, and the environment. Furthermore, she identified compounds that were related to sexual receptivity and used these pheromones in applied conservation of captive and wild populations. Abbey spent a summer in China implementing techniques developed during her Ph.D. research to better understand how giant pandas communicate in the wild. In her spare time, Abbey enjoys traveling, horseback riding, and hiking with her dog, Bella. She has especially enjoyed exploring the back country of Kananaskis Country and Banff National Park in Canada. Abbey’s work with the Grizzly-PAW project aims to apply and further validate novel biomarkers of physiological function in free-ranging grizzly bears by (1) developing a liquid chromatography tandem mass spectrometry multiple reaction monitoring (LC/MSMS/MRM) method to identify and quantify the expression of proteins in skin that are associated with energetics, reproduction, immune function, and stress and (2) determining concentrations of hormones in hair that are associated with reproductive status (testosterone, progesterone, and estradiol) and long-term stress (cortisol). Once developed and validated, these novel tools will generate expression profiles of multiple proteins and hormones associated with physiological homeostasis that may provide sensitive and reliable markers of health and reproductive status in bears.
Sean Kearney’s research is primarily within Theme 1, mapping anthropogenic and natural disturbance patterns to provide more detailed inputs for analyzing grizzly bear behaviour and physiology. One of the key research activities within this theme is to improve existing road network maps for the Yellowhead bear management area (BMA 3). This research involves two main objectives, (1) automatically detecting roads using high spatial resolution satellite imagery and (2) classifying roads to reflect their use intensity (i.e., traffic).
Research has shown that the impacts of roads on large mammals are a function of both road density and traffic. However, traffic data is rarely available for rural roads, and can be difficult to estimate, especially in multi-use public lands. This lack of data limits our ability to understand the impacts of rural roads on wildlife, and restricts the policy options available to land managers. Remotely sensed and crowd sourced datasets provide an opportunity to estimate the timing and intensity of road use based on the condition of roads and the most likely destinations of people using them.
For the first objective, Sean is employing machine learning to train a computer to detect roads in a high spatial resolution satellite image (RapidEye sensor, 5 m resolution) acquired in 2017. This work began in September 2018 and is being supported by newly hired worklearn student Simran Sethi, a graduate student from UBC’s Data Science department. Additional work is needed to parameterize the machine learning algorithm, but initial results are promising.
For the second objective, Sean is modelling the use-intensity of road segments, based on where people are coming from, where people are going and the distance and road surface conditions between start and end locations. Start locations have been identified from nighttime lights intensity detected by satellite, and end locations and their use-intensity are being identified using social media posts, recent disturbances (e.g., logging activity) and anthropogenic features (e.g., well pads, campgrounds). The modelling approach will predict road use intensity based on the connectivity between each of these points and the road network paths and conditions. Road surface conditions will be extracted from RapidEye satellite imagery using training data collected from the accelerometer of mobile devices via an app called RoadLab-Pro.
Road condition data was collected by fRI in the summer of 2018 and by industry partners over the 2018/2019 winter season. An initial analysis has been conducted for this region to develop and test the modelling algorithm (Figure 1) and work is ongoing to expand the analysis to the entire Yellowhead region and link the new road network maps to grizzly bear movement and physiology datasets. Additional road surface condition data collection is currently being conducted by several industry partners to expand the analysis and validate results.
We anticipate that this updated road network map will improve our understanding of how road locations, and the timing and intensity of their use, are affecting bear physiology, behaviour and mortality. We also hope to better understand the relative influences of industrial versus recreational road use on grizzlies, which could help to inform policies related to road construction and access closures.
The Grizzly-PAW project team would like to thank Westmoreland Coal, TransCanada Pipelines, Shell Canada and Canfor for their continued efforts to collect road data and support this research.
Figure 1. The test area and initial results of mapping road use
Panel (a) shows the high-spatial resolution RapidEye satellite image acquired in 2017. Panel (b) shows the existing road network map overlaid with source and destination points extracted from multiple social media sites, sized based on their activity levels. Only points within 1km of roads are shown, and points within 1km of each other have been combined to reduce processing time. Panel (c) shows road surface roughness as tracked on select roads using the accelerometer and GPS units of mobile devices equipped with the RoadLab-Pro app. Panel (d) shows the modelled road use based on the connectivity and activity of points shown in (b).
In-kind Support Activities
We are currently gathering data for snow melt dynamics (Q1.2) and road usage (Q1.1). Although our snow melt researcher, Ethan Berman, defended his thesis on Jan. 23rd, the data collected will still be useful for calibration of the snow melt product that was generated through his research. According to Ethan, data gathered for this winter will assist us in checking the validity of the snow data as we continue to update it for new years. The snow product is still updating on an annual basis (every summer the previous winter will be computed) but there remains potential to re-construct the product to update continuously throughout the year. As described in the research highlight Sean Kearney is actively using the road data collected by our industrial collaborators. Bethany Arndt is also using that data to investigate how visibility affects grizzly bear movement around roads. Our team appreciates the continued support of our industrial collaborators in collecting these valuable field data.
Grizzly-PAW Y2 AGM
Grizzly-PAW’s second AGM was held on Oct. 18-19, 2018. This time, the AGM was held in Hinton, AB. All students and postdocs working on the project presented on the first day followed by a resource development discussions at the end of day. On the 19th, the participants went into field sites located close to Hinton and demonstrated methods for bear capture mechanics, snow melt monitoring, phenological monitoring and songbird data collection.
This AGM had 26 attendees, with all active PAW researchers in attendance, as well as many of the industrial partners and government researchers and staff.
The next AGM will likely be in October 2019. Dates and location are yet to be determined. In addition to presentations on research results, this year’s AGM will include a showcase session. Watch out for more details about the AGM in the next issues of Tracks.