Grizzly PAW’s Electronic Newsletter
Vol. 3, Issue 3. Date: 2019-06-18
paw.forestry.ubc.ca

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Welcome to the Spring edition of Tracks. Congratulations to Bethany Parsons for being one of the winners in UBC Forestry’s Three Minute Thesis competition. Bethany’s presentation was related to her work on Q3A.4. Good job Bethany!

In this edition:

Research Update

Feature Researcher

Research Snapshot

In-Kind Contribution

Research Update

Q1.1 Broad Scale Mapping of ecoAnthromes / Road detection

Sean Kearney completed the preliminary analysis to model the use-intensity of road segments using social media posts, recent forest harvest disturbances, well sites and road network connectivity. He presented the results of this analysis at the Global Land Programme’s Open Science Meeting in Bern, Switzerland in April. Sean continue supervising the work of a student to develop a machine learning algorithm to automatically detect roads from RapidEye satellite imagery.

Q1.2 Snow-melt Dynamics

Ethan Berman successfully defended his MSc thesis in January 2019, competing Q1.2. He has published a paper that details the development of fine-scale snow mapping product and the analysis of how bears interact with snow during spring. Ethan also published a second paper titled “Grizzly bear response to fine spatial and temporal scale spring snow cover in western Alberta”.

Q1.3 Grizzly Bear / Song Bird Surrogacy

Emily Cicon completed the statistical analyses of her ARU studies and the umbrella species potential of grizzly bears vs. other flagship spp. She is currently editing her thesis after it has been reviewed by her supervisor.

Q2.1 Yellowhead Grizzly Bear Population Demographic Analysis

Greg Rickbeil modeled different metrics of snow melt and linked them to spring bear activity date. He also estimated bear activity dates across BMA 3 for 2001-2016 and has begun writing a manuscript on the results.

Q2.2 Modelling and Simulating Grizzly Bear Food Supply

Chris Souliere is finalizing for submission the chapter 1 manuscript on Q2.2 regarding food supply in fire and harvest. He started coding an individual-based model looking at top-down and bottom-up factors and started to explore techniques to be used in forest optimization model.

Q3A.1 Bear Movement

Completed.

Q3A.2 Forest Structure and Movement

Brandon Prehn drafted his first manuscript on relationships between bear movement and LIDAR-derived vegetation attributes. He took comments from 4 coauthors and revised his methods, reinterpreted the results, and has redrafted a second version of the manuscript. He also revised his analysis methods. Brandon completed his course on Fluvial Ecohydrology with an A- grade!

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

Cam McClelland completed analysis of 30m spatial resolution daily vegetation products and continues his analysis of grizzly bear movement in relation to vegetation. He submitted his first paper to his committee members and co-authors for review.

Q3A.4 Grizzly Bear Movement and Roads

Bethany Parsons performed movement analyses for bears using viewshed and started working on python code to perform soundshed analysis. She presented a poster at the Movement Ecology of Animals Conference in March 2019.

Q3A.5 Contextualizing Movement and Roads

Greg Rickbeil has taken on this role as part of his PDF project.

Q3B.1 & 2 Physiological Markers

Abbey Wilson applied what she learned in the lab on hair hormone extraction to extract and analyze grizzly bear hair samples that were collected in 2004. She then identified target proteins indicative of grizzly bear health and their corresponding proteotypic peptides for the development and validation of the LC/MSMS/MRM assay. Abbey also completed data evaluation and statistical analysis for 2004-2014 HCC manuscripts. She submitted the abstract to the Wildlife Society for a presentation in its September 2019 conference.

Feature Researcher

Cam McClelland grew up just outside of small-town Hinton, Alberta. Having spent most of his young life exploring the backcountry of the Wilmore Wilderness Park on horse back he knew he wanted to pursue a career in wildlife research and conservation. After completing his BSc in Environmental Earth Sciences at the University of Alberta he was able to realize this goal with the fRI Research Grizzly Bear MProgram. Cam spent four summers and a one year internship with the program working on projects ranging from population dynamics to predation. When not working with the fRI Cam split his time between travelling and skiing.
Wishing to expand his knowledge base, Cam will be working with the IRSS lab as an MSc candidate. He will be assessing how changing vegetation patterns are affecting food availability and how this influences space-time patterns of grizzly bear movement at fine spatial and temporal scales.

Research Snapshot

Abbey Wilson’s research is focused on wildlife health, and more specifically Theme 3B: Physiology of the Grizzly PAW project. This theme aims to address the question: Have changing landscape conditions associated with anthropogenic natural resource extraction activities resulted in changes in habitat selection by, and the health of, grizzly bears within the study area? Therefore, the goal of this theme is to create a novel tool to monitor species-at-risk on changing landscapes that can be communicated efficiently with industry partners. We plan 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).

Protein biomarkers have been identified in captive species such as cheetahs, several canid species, and polar bears. However, this is the first study to identify and quantify protein expression related to physiological function in free-ranging grizzly bears. Our team has developed an extraction technique to isolate and purify sufficient protein from small (<0.1g) skin biopsies to use in the LC/MSMS/MRM assay. Three skin samples were shipped to University of Victoria Genome British Columbia Proteomics Centre for initial discovery analyses using a Thermo Scientific EASY-nLC II coupled with an LTQ Orbitrap Velos-Pro mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). This initial analysis identified amino acid sequences from over 600 proteins in grizzly bear skin. These proteins were compared to the Universal Protein Resource (UniProt) to identify homologous sequences found in other species of Ursidae (bear family). Nineteen of these proteins were chosen as targets for analysis because of their roles in energetics, reproduction, and stress. The LC/MSMS/MRM method is currently being developed and validated in order to quantify these target proteins in grizzly bear skin.

Over the past five years, we have been developing and validating laboratory techniques to measure several steroid hormones, indicative of long-term stress and reproductive function in the hair of grizzly bears in a reliable and feasible manner that would enable routine application for grizzly bear health assessments. We are now able to measure cortisol, estradiol, progesterone, and testosterone with precision (intra- and inter-array coefficients of variation <10% and <15%, respectively) in small amounts of hair (100 mg) that can be readily obtained using barbed-wire hair snags. Recently, our team has determined that hair cortisol concentration (HCC) is associated with landscape variables related to anthropogenic footprint, food resource availability, and landscape conditions in 2004 and 2014, when two large scale population inventory projects took place. Distance to cutblocks and coal mines had a direct relationship with HCC, while distance to roads and well sites, road density, and percent protected area had an inverse relationship with HCC. Crown closure and distance to non-vegetative cover had a direct relationship with HCC, while percent conifer and distance to regenerating forest and upland trees had an inverse relationship with HCC. Percent conifer and percent crown closure explained the most variation in the model, indicating that increased food availability was associated with decreased HCC. Therefore, changes in landscape management from 2004-2014 may be decreasing long-term stress in grizzly bears by indirectly increasing food abundance.

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. These novel techniques also have a broader application to any species at risk and can be used as sensitive conservation tools to detect new threats to the health of individual animals well in advance of population-level effects by providing an early warning of population decline. Therefore, industry partners will be able to more effectively monitor the health of wild species inhabiting areas associated with industry activities and establish baseline health information on wild species inhabiting areas identified for future industry activities. By developing these techniques, we aim to create a novel method by which scientists and managers around the world can further monitor species-at-risk that reside on changing landscapes. Our team thanks our industry and academic partners for continuing to support this research.

In-Kind Contribution

We appreciate our industrial partners’ continued support in collecting and providing our HQPs with field data. Sean Kearney received from our partners data collected from mobile devices to develop a final calibration/validation dataset to improve the final model, assess accuracy, and ultimately apply it to the entire Yellowhead study area. From these data, Sean, in coordination with Greg Rickbeil, was able to organize several datasets to begin analysis on the spatial and temporal patterns of human activity within the study area.