Recipient Organization
UNIVERSITY OF WASHINGTON
4333 BROOKLYN AVE NE
SEATTLE,WA 98195
Performing Department
Wildlife Science
Non Technical Summary
In 2008, gray wolves were documented as breeding in the state of Washington for the first time in roughly 80 years. The wolf population in Washington has since grown to roughly 50 animals in 12 packs. The return of this top predator offers an opportunity to study associated changes in prey populations and ecosystems. Beginning in 2012, with funding from the National Science Foundation, we initiated a study of mule and white-tailed deer responses to recolonizing wolves in north-central Washington. Our ultimate goal for this study is to determine whether these two deer species, which have divergent means of escaping canid predators, respond differently when confronted with the threat of wolf predation. If so, then wolves could indirectly affect plant communities via two separate pathways, one driven by foraging changes specific to mule deer, and the other by foraging changes specific to white-tailed deer. This study will run through 2017.As part of this larger effort, this project will refurbish and deploy GPS collars on mule and white-tailed deer during our third year of work (December 2014-November 2015), in order to explore seasonal patterns of spatial interactions between these prey species and wolves. During the same time period, we will deploy vaginal implant transmitters on pregnant does, to identify birth events, and subsequently breakaway VHS collars on newborn fawns to monitor patterns of fawn mortality in areas with and without wolves.
Animal Health Component
(N/A)
Research Effort Categories
Basic
100%
Applied
(N/A)
Developmental
(N/A)
Goals / Objectives
The goal of this multi-year project is to understand how recolonizing gray wolves might impact mule and white-tailed deer via non-consumptive (i.e., predation-risk causing shifts in habitat use, foraging behavior, herding, etc.) and consumptive (i.e., direct killing of deer causing changes in adult or fawn survival, population growth, etc.) effects. We also plan to investigate how wolves can potentially influence range conditions by changing deer behavior, abundance, or both, and thereby potentially reducing deer herbivory and impacting range biomass and plant composition. Furthermore, wolves in our study areas are already radio-collared. Thus, there is great scope for research into wolf habitat use, food requirements, and overall behavior within a managed landscape.
Project Methods
We have four study areas: two with and two without wolves. In the two areas with wolf packs, there are already GPS collared wolves. Below, we detail how we will meet our research objectives during the year (2014-2015):For Objective 1, our goal is to use live traps to capture and deploy camera collars on 40 deer during the winter of 2014-2015 (four study areas combined). Camera collars fitted to deer are set to record six hours of footage each day for two weeks, meaning we have the potential for 84 hours of footage per deer. After two weeks remote drop off units will allow for retrieval of collars and subsequent redeployment. We have 20 camera collars, and each will be deployed up to 3 times per winter. Fine-scale foraging and behavioral data from the cameras will allow us to rigorously address the fear effects of wolves on deer. Concurrent GPS locations from wolves and deer will reveal how each species moves in relation to the other.To achieve Objective 2, we will use ultrasound equipment to assess body condition of captured deer and determine pregnancy in captured females.For Objective 3, we hope to deploy GPS collars refurbished using funds from this request on 40 deer. GPS collars will be set to record locations twice a day and will likely last for >3 years because they are survey collars with much longer battery life than traditional GPS collars. Locations will be taken every 12 hours and 5 minutes to aid in getting around-the-clock behavior of deer. Collars will provide a large data set on annual habitat use by both mule and white-tailed deer. Collared deer will be monitored and if individuals die, cause of mortality will be determined. Any does that are captured and determined to be pregnant will be implanted with a VIT tag, allowing us to locate fawns when they are born. Upon finding a newborn fawn via the VIT tag, we will then place a VHF break-away collar on the fawn. This procedure will allow us to better determine cause of death in the event of mortality and give us an overall estimate of fawn survival. All deer captured are ear-tagged for identification. We have set up camera grids in each of the four study areas to conduct a capture-mark-recapture analysis based on 'recaptures' or photos of ear-tagged deer. Photos of ear-tagged deer, coupled with information on which ear-tagged does were pregnant at the time of capture, will allow for estimation of adult and fawn survival, as well as seasonal abundance estimates. This camera method of estimating fawn survival will provide for an interesting comparison with datafrom the VHF break-away collars. Camera grids will also allow for estimating relative abundance of the various ungulate species (as well as other taxa) in the study areas.For Objective 4, wolf location data from GPS collared individuals in packs will be analyzed for location clusters, potentially representing kill sites. We will visit these clusters to determine patterns of prey selection. To account for other factors that might influence deer behavior or survival, we plan to monitor weather and vegetation in each study area. We have purchased and plan to deploy a weather station in each study area. These stations will monitor and record wind direction and speed, relative humidity, temperature, precipitation, snow depth, and sun light intensity every hour while deployed. Deployments will span three years. We will use MODUS-1 satellites to collect data on vegetation quality and abundance. MODUS-1 satellites take images of the earth (the same places every 16 days) and measure large-scale (1 square kilometer resolution) ground reflectance. Reflectance varies with presence and amount of vegetation, allowing for determination of vegetation abundance at 16-day intervals. Changes inreflectance are directly correlated with changes in vegetation quality.For Objective 5, we will erect 160 5m x 5m exclosure plots, 40/study area. Exclosure plots will be immediately adjacent to control plots, which will be monitored by cameras in an attempt to determine which ungulate species are foraging most heavily within different habitat types. Control plots will be compared to exclosure plots based on a number of variables including: tree sapling density, recruitment, and percent ground cover.