Grizzly PAW V4 Issue 1 (November 2019)

Grizzly PAW's Electronic Newsletter
Vol. 4, Issue 1. Date: 2019-11-19

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Previous Issue

Welcome to the summer and fall edition of Tracks.
Summer was busy time for field work and preparation for the Grizzly-PAW AGM. The HQPs were mostly out in the field to gather data and interact with industry partners. During this time, questions Q1.1, Q1.3, Q2.1 and Q3A.1 were completed. Emily Cicon successfully defended her thesis and graduated in May while Greg Rickbeil and Sean Kearney both have moved on to new employers. We congratulate them in their successes and we thank them for their contributions to the GPAW project.

In this edition:

Research Update

Q1.1 Broad Scale Mapping of ecoAnthromes / Road detection

Sean Kearney completed the analysis to automatically detect roads and update the existing road network for BMA 3 using RapidEye satellite imagery. A manuscript on this work was submitted to the International Journal of Applied Earth Observation. Sean is likewise finalizing another manuscript on the analysis to model the use-intensity of road segments using social media posts, recent forest harvest disturbances, well sites and road network connectivity. He will continue to collaborate with the research team to make all necessary revisions on this manuscript and submit for publication.

Q1.2 Yellowhead Snow Melt and Spring Flush Dynamics


Q1.3 Grizzly Bear / Song Bird Surrogacy


Q2.1 Yellowhead Grizzly Bear Population Demographic Analysis


Q2.2 Modelling and Simulating Grizzly Bear Food Supply

Chris Souliere built an individual-based model looking at top-down and bottom-up factors which currently has a working preliminary simulation model. He finalized the chapter 1 manuscript on Q2.2 about food supply in fire and harvest and submitted to Forest Ecology and Management for review.

Q3A.1 Movement and Health


Q3A.2 Forest Structure and Movement

Brandon Prehn submitted his manuscript on the relationships between bear movement and LIDAR-derived vegetation attributes to Journal of Applied Science. He is now working on finalizing his thesis and looking to defend in the winter term.

Q3A.3 Phenology of Grizzly Bear Foods and their Relationship to Movement

Cam McClelland begun work on relating grizzly bear movement to vegetation product with annual food layers generated. His first paper titled Detecting changes in understory and canopy phenology in West Central Alberta using a fusion of Landsat and MODIS, was submitted to Journal of Applied Vegetation Science for publication.

Q3A.4 Grizzly Bear Movement and Roads

Bethany (Arndt) Parsons conducted a modified semivariogram analysis, looking at variation in grizzly bear movement patterns to determine at what distances grizzly bears respond to roads and whether or not the bear can perceive the road. She completed the soundscape layer and also submitted her first paper titled "Building a perceptual zone of influence for wildlife."

Q3A.5 Contextualizing Movement and Roads


Q3B.1 & 2 Physiological Markers

During spring field season, Abbey Wilson collected tissue and hair samples from free-ranging grizzly bears with FRI. She processed, extracted, and analyzed the tissue samples for LC/MSMS/MRM protein assay. Other tasks she completed are hormone extraction and analysis of hair collected and the development and validation of the LC/MSMS/MRM assay for tissue analysis. Abbey is finalizing her manuscript titled: Population level monitoring of long-term stress in grizzly bears between 2004 to 2014, for journal publication. She was also successful in getting the Mitacs Accelerate award with funding of $45,000 CAD.

Grizzly PAW AGM

The 2019 Grizzly-PAW annual general meeting was held on Oct. 18th at the TC Energy headquarters in Calgary Alberta. Among the thirty five attendees, eight were from government agencies and 14 from four industry partners. The day was divided into two parts, with the morning session devoted to the AGM presentations while the afternoon was for the project overview and future direction sessions. The afternoon was opened to guests of Grizzly PAW. Delightful feedback came from the attendees regarding how they see all the research questions coming together and interconnected. The research’s future direction looks very promising. Copies of the AGM presentations can be viewed from the Grizzly PAW website.

Feature Researcher

Growing up in an outdoorsy family in the outdoorsy little city of Nelson, BC, Bethany (Arndt) Parsons had little choice but to fall in love with lakes, rivers, mountains, and forests. She loves exploring and discovering more about the intricacies and beauty of the natural world, which she considers God’s Masterpiece. She decided that a career in wildlife and conservation sounded like a good excuse to spend more time in and learning about nature, and has so far enjoyed her summers working in environmental consulting. Bethany completed her undergraduate degree in Biology at UBC Okanagan in Kelowna before heading to UBC Vancouver to take up masters. She is now in her second year of her graduate studies at the IRSS lab where she is building models of grizzly bear response to roads. Although it was initially a challenge to navigate a campus six times the population of her home town, Bethany enjoys finding ways to escape the business of Vancouver with her husband Adam by hiking, biking, and kayaking in the beautiful surroundings.

Research Snapshot

Cam McClelland’s research is focused on theme 3A: Phenology of Grizzly Bear Foods and their Relationship to Movement. In this section we focus first on detecting long term variations in phenology at a fine spatial and temporal scale over the entire Yellowhead region. Second, we will relate this annual regional phenology to individual vegetative bear food species and determine how variations in availability of specific species influences grizzly bear movement and habitat selection.

Vegetation phenology has been determined to influence habitat selection and movement of many species including grizzly bears. In order to monitor vegetation phenology for the entire Yellowhead region we developed methods that utilises two remote sensing platforms. The first platform is MODIS, which provides daily earth monitoring with a spatial resolution of 500m. The second platform is Landsat, which has a 16-day return time (often prolonged with cloud cover) and a 30m spatial resolution. In order to accomplish our goal of a daily, 30m product from 2000-2018 over the Yellowhead region we fused these two platforms using the Dynamic Time Warping algorithm. The resultant product is entitled DRIVE (Daily Remote Inference of Vegetation). Through DRIVE we were able to determine that the start of growing season has been beginning earlier throughout the past two decades throughout elevation and land cover classes. We will utilize these results to determine whether variations in vegetation cycles are affecting grizzly bear movement.

fRI Research has been tracking grizzly bear movement since 1999 through the use of GPS collars. We will use this collar data to compare movement of grizzly bears across the landscape to results from DRIVE to determine how the timing of start and end of growing season influences grizzly bears. We also wish to see whether changes in individual food species are influencing grizzly bear movement. To accomplish this we have modelled annual species specific food layers for eight bear food species using maximum entropy models. For each species we extracted species availability using ground data and DRIVE. Through the use of these food layers and a resource selection function we will determine whether the variation in availability of specific bear foods is affecting habitat selection and movement for grizzly bears.

With this research our goal is to provide industry partners with valuable information on how vegetative food resources are changing across our landscape and how grizzly bears are being affected by these changes. While this research is focused on grizzly bears our results have broader application possibilities and may be able to provide insight in to other wildlife species and even provide insight into a variety of different questions regarding forestry and industry practices. By developing these techniques we have created a method for which industry partners can monitor phenology across their respective landscapes and provide information on how to best manage important assets while protecting key wildlife resources.

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