2017 Kootenay Remote Camera Multi Species Occupancy Project 2016 17 Progress Report Tara Fish and Wildlife, Ministry of Forests, Lands and Natural Resource Operations 1/1/2017
EXECUTIVE SUMMARY The Kootenay Region has a great diversity of wildlife including seven ungulate species (mountain goats, bighorn sheep, moose, mule deer, white tailed deer, caribou and elk) and seven large carnivores (black bear, grizzly bear, wolf, coyote, cougar, lynx, and bobcat). The regional wildlife and habitat biologists monitor trends for these large mammals to ensure sustainable use of harvested species and to develop or refine management objectives for all species and habitats in a changing landscape. Stakeholders and First Nations in the Kootenay Region have repeatedly called for multi species monitoring and early detection of changes in distribution and population size. The purpose of this project is to help meet this societal demand for science based management of the Kootenay Region s most celebrated wildlife species. The Kootenay Remote Camera Wildlife Monitoring Project has four main objectives: 1) monitor trends in large mammal occupancy over time, 2) assess the relationship between trends in occupancy and abundance, 3) estimate the density of wolf populations, and 4) determine the effectiveness of remote camera traps to estimate grizzly bear population density. In combination with current cameras deployed in the surrounding National Parks, the addition of the Kootenay Region creates a large network of cameras throughout the Rockies. Cameras will be deployed over a 5 year period using sampling design and analysis developed in the surrounding National parks. In 2016, 31 cameras were deployed in the Flathead (MU 4 01) and Wigwam (4 02) and 32 were deployed in the Elk Valley (MU 4 23) in 2017. Finally, we will deploy 25 cameras in the Bull River (MU 4 22) in the summer of 2018. They were deployed at locations to maximize the probability of detection, such as narrow valleys, pinch points, and creek forks, and at important wildlife features such as mineral licks and grizzly bear rub trees. Discrete events were classified based on species, sex, and age classes using TimeLapse. 1394 events were captured on 28 cameras between July 20 and November 18, 2016 in the Flathead. White tailed deer had the most events (361, with 581 animals), with mule deer second (198, 415 animals), followed by moose (147 events, 202 animals), elk (98 events, but 209 animals). Photos from the Elk Valley will be classified over the winter of 2017/18, and preliminary results on densities and occupancies of different species will be available in 2019, after two years of data collection. 1
TABLE OF CONTENTS Executive Summary... 1 Introduction... 3 Objectives... 4 Broader Regional Perspective (Mountain Parks, Y2Y)... 5 Partnerships... 5 Methods... 6 Study area... 6 Camera Settings... 11 Field Methods... 12 Photo Classification... 13 Data Analysis... 13 Occupancy Trends... 13 Abundance trends... 14 Wolf population densities... 14 Grizzly bear population density... 15 Results and discussion... 16 Flathead Wigwam... 16 Elk Valley... 19 Ongoing Work... 19 Acknowledgements... 19 Literature Cited... 20 Appendix... 20 2
INTRODUCTION The Kootenay Region has a great diversity of wildlife including seven ungulate species (mountain goats, bighorn sheep, moose, mule deer, white tailed deer, caribou and elk) and seven large carnivores (black bear, grizzly bear, wolf, coyote, cougar, lynx, and bobcat). The Kootenay Region Fish and Wildlife section monitors trends for these large mammals to ensure sustainable use of harvested species and to develop or refine management objectives for all species in a changing landscape. The Kootenay Region Ecosystem section also requires information on the size and trend of wildlife populations to prioritize habitat conservation and recovery activities, and monitor effectiveness of restoration efforts in the region. Estimating wildlife populations and tracking population trajectories is an ongoing challenge in wildlife ecology. Traditional monitoring techniques such as radio collaring and aerial surveys are expensive and cost prohibitive given limited budgets, and hence most species are monitored periodically at best. Remote camera monitoring projects are non invasive, cost effective, and enable monitoring multiple species at once. Occupancy itself is a useful index on changes in population trends over time. Using cameras, trends in distribution (occupancy) are detected more quickly than by traditional methods allowing for a more timely management response. Cameras can provide reliable information about the spatial distribution of multiple species over large extents for relatively low investment. Alone and in combination with existing techniques (radio collars, aerial inventories), remote camera monitoring can provide powerful information to natural resource decision makers about wildlife populations and their habitats. Stakeholders and First Nations in the Kootenay Region have repeatedly called for multi species monitoring and early detection of changes in distribution and population size. The purpose of this project is to help meet this societal demand for science based management of the Kootenay region s wildlife. 3
OBJECTIVES The Kootenay Remote Camera Wildlife Monitoring project has four main objectives: 1. Monitor trends in large mammal occupancy over time. This will answer some of the most pressing questions from stakeholders, such as Have elk populations declined in or disappeared from particular watersheds? or Has the distribution of wolves increased over time?. If sufficient sample sizes are available, recruitment of juveniles may also be used to indicate population trend. 2. Assess the relationship between trends in occupancy and abundance. Abundance data from aerial surveys in the Elk Valley (e.g., annual surveys on Teck Ltd mine properties, FLNRO moose inventories, Sparwood Rod and Gun Club elk monitoring) will be correlated with occupancy data from remote cameras. If a statistical relationship between occupancy and abundance can be established, even for a few species, it will enable us to relate changes in occupancy (using relatively inexpensive remote cameras) to real changes in population trends over time. 3. Estimate the density of wolf populations. This is possible for wolves using remote cameras since some individuals within packs are uniquely identifiable by coloration, and given recent advances in spatial mark resight methods developed for partially marked populations. 4. Determine the effectiveness of remote cameras to estimate grizzly bear population density. The proposed study area overlaps with grizzly bear DNA mark recapture studies and radio collaring projects, providing a unique opportunity to compare findings from different monitoring programs. Again, collaborating projects in the Canadian Rocky Mountain National Parks have developed partially marked spatial mark resight models to estimate grizzly bear density. 4
BROADER REGIONAL PERSPECTIVE (MOUNTAIN PARKS, Y2Y) Remote camera data has been collected from 2011 and 2014 from Banff, Kootenay, Yoho, Jasper, and Waterton National parks. Within these five parks, 270 cameras have been deployed year round to collect data on multiple species over a large spatial scale. Steenweg et al. 2015 identified three key parameters to maximize the utility of remote cameras: a large number of cameras, long deployment duration, and a large spatial scale. The addition of the Kootenay Remote Camera project to the existing framework of cameras in the mountain parks increases the number of cameras and widens the spatial extent of the existing camera network creating a large network of cameras throughout the Rockies. PARTNERSHIPS This project is a partnership between the University of Montana, University of British Columbia, Parks Canada, and the Kootenay Fish and Wildlife Section. In addition, we are working closely with volunteers from the Southern Guide Outfitter association, local Rod and Gun clubs and conservation organizations. Volunteers have been recruited to assist with camera deployment and maintenance. Currently, we have five volunteers from the Elk River Alliance, Sparwood Rod and Gun Club and South Rockies Grizzly Bear Project that are assisting with nine cameras in the Elk Valley. Several existing projects in the study area allow for data from various sources to be compared, which will strengthen analyses. Current projects include: Elk monitoring in the Elk Valley (Sparwood Rod and Gun Club, >40 radio collared cow elk) Multi species aerial inventories in the Elk Valley (conducted annually by Teck) South Trench elk monitoring (FLNRO, 3 elk radio collared in MU 4 02) Grizzly bear monitoring in the Flathead and Elk Valley (FLNRO, rub tree monitoring, 18 radio collared bears) Moose inventory in the Flathead/Wigwam (2017) and Elk Valley/Bull River (2018) Mule deer monitoring (FLNRO, >20 deer radio collared in MU 4 02) Wolverine monitoring (camera trapping and hair samples collected by FLNRO/T. Clevenger in MU 4 02) Wolf monitoring in the Flathead and Elk Valley using bioacoustics (UBC O MSc student with Dr. Adam Ford) 5
METHODS STUDY AREA The Kootenay Remote Camera Wildlife Monitoring Project study area is located in the south eastern corner of BC in the East Kootenay (Figure 1). The project was initiated in the Flathead (MU 4 01) and Wigwam (MU 4 02) in 2016 (Figure 2), and was expanded to include the Elk Valley (MU 4 23) in 2017 (Figure 3). Next year (summer 2018) the project is planned to expand into the Bull River (4 22; Figure 4). These areas were selected because of overlap with other inventory and monitoring projects, and adjacency to a remote camera monitoring program in the Mountain National Parks (Banff, Kootenay, and Waterton), Alberta Provincial Parks, and Alberta Crown Land (Figure 5). In summer 2017, the Bull River drainage had several large wildfires, and this project will greatly enhance post fire wildlife monitoring. Figure 1. The study area (yellow) for the Kootenay Remote Camera Wildlife Monitoring project, covering Wildlife Management Units 4 01/4 02 (established in 2016), 4 23 (established in 2017) and 4 22 (planned for 2018). 6
Legend Camera Forest Service Road Wildlife Management Unit Figure 2. The study area and camera locations (red dots) for Management Units 4 01/4 02 (Flathead/Wigwam, established in 2016). 7
Legend Camera Forest Service Road Wildlife Management Unit Figure 3. Study area and camera locations (red dots) for Management Unit 4 23 (Elk Valley, established in 2017). 8
Legend Forest Service Road Wildlife Management Unit Figure 4. Proposed study area and 10x10 km grid for the 2018 Bull River (MU 4 22) expansion of the Kootenay Remote Camera Wildlife Monitoring Project. 9
Figure 5. Study area for the Canadian Rockies Remote Camera Species Occupancy Project, 2011 2015. 10
CAMERA SETTINGS Camera settings were the same among all cameras, and were designed to maximize detection and photo quality during the day and at night. They were set to take five consecutive photos when movement was detected, using the highest sensitivity for movement with no delay period. Each camera took one photo at 1200 PM every day to ensure the camera was functioning correctly through the whole deployment period. Table 1. Camera settings used for the Kootenay Remote Camera Wildlife Monitoring Project (within camera settings). Function Sub menu Setting Trigger Motion Sensor ON Sensitivity HIGH Pics per Trigger 5 Picture Interval RAPIDFIRE Quiet Period NO DELAY Time Lapse AM Period OFF PM Period ON Start Time 12:00PM End Time 1:00PM Interval 1 HOUR Resolution 1080P Night Mode Max Range Max Range Illuminator ON Date/Time/Temp Date/Time Set as current date/time Temp Celsius 11
FIELD METHODS We will deploy and collect data from remote cameras over a 5 year period (2017 21) to allow for analyses of trends over time. Initial years will identify distribution and possibly density for some species (e.g., wolves, grizzly bears). The sampling design and analytical methods were adopted from those established in adjacent Mountain National Parks (Steenweg et al. 2015). Four years of camera data collection in five National Parks have resulted in a solid study design that can be applied to our area. Provincial Resource Information Standard Committee standards (https://www.for.gov.bc.ca/hts/risc/pubs/tebiodiv/index.htm) for remote camera projects are also currently being developed in collaboration with our region (Tyler Muhly, Natural Resource Modeling Specialist, FLNRO, personal communication). These standards are followed for our project. Cameras were placed at a density of 1/100km 2 (one camera per 10x10km grid cell). This allows for relatively even and complete coverage across the study area, while still being balanced with cost and field logistics (Steenweg et al. 2015). Camera locations were picked using a combination of landscape and site specific features to return the highest probability of detection (POD). Targeted landscape level features include pinch points, narrow valleys and creek forks. Targeted site specific features include grizzly bear rub trees, mineral licks, and game/guide/horse trails. Local knowledge was also a valuable asset used to pick camera locations. Covert, motion triggered cameras were used (Reconyx PC900, PC800, HC600, HC500) and were mounted on trees with the intention of maximizing photo quality of animals of all sizes, while still being as covert as possible. Cameras were set on trees between 2 5m off the trail, between waist and shoulder height, and angled slightly downward to capture images of smaller animals (badger, wolverine, fox, etc.). They were secured using bungee cords with padlocks, or python locks. Any small vegetation or branches in the view of the camera were removed to limit false triggers of the camera. Physical and scent disturbance was minimized in the area of the camera during deployment. Most cameras are operational year round, though some are inactive in the winter due to deep snow, or battery death. In 2016, 31 cameras were deployed in the Flathead and Wigwam, and an additional 32 were deployed in the Elk Valley in 2017. Elevations ranged between 700 2173m, with an average of 1433m. Fifteen were on human use trails, ten on road beds, and the remainder on wildlife trails. Ten of those were placed facing mineral licks and sixteen facing or near bear rub trees. 12
PHOTO CLASSIFICATION Rather than classifying each image, discrete events were classified by species, sex, and age classes using Timelapse (Greenberg and Godin 2012). Discrete events were defined by individual or groups of animals that occur at least 5 minutes after a picture of the same species unless it is clear that they are different individuals (Steenweg et al. 2015). For example, if the same deer or group of deer appear in multiple images in a row, or pass by the camera multiple times, those images would be defined as one discrete event. If passages were separated by 10 minutes of more with clear separations in between, it would be defined as two discrete events. Additionally, any radio collared or ear tagged animals were noted. Images of people were recorded by activity (eg. hiking, horse riding, etc.) and deleted to address privacy concerns. The data is then run through an R script created by Park Canada, designed to pull out inconsistencies and errors in tagging. DATA ANALYSIS OCCUPANCY TRENDS We will follow methods developed by Parks Canada (Steenweg et al. 2015) for multiple species to model trends in species occupancy during winter and summer. We will build occupancy and detection probability models for large mammals in the Kootenays using landscape (GIS) and site covariates, following methods in Whittington et al. (2016). We will initially adopt covariates found to be significant for the Rocky Mountain National Parks. These included a mix of biotic (e.g., crown closure, presence of rub tree, land cover type, mineral lick), abiotic (e.g., elevation, slope, aspect), and anthropogenic (e.g., human activity on trails, distance to roads/rail, trail type) covariates. Jesse Whittington (Parks Canada) will run a global multi year occupancy model with region specific estimates of detection probability and occupancy. He will use the same covariates for all areas and run separate models for summer and winter. Thus, we will derive both regional and large scale occupancy estimates from the same model. More than one year of data will be required to estimate trends in occupancy over time. Hence these analyses will start in 2019, following 2 years of data collection. 13
ABUNDANCE TRENDS Independent data on wildlife populations will be compared to remote camera data to determine whether occupancy trends from remote cameras correlate with wildlife population trends. There are several opportunities to compare these independent data sources in our study area: Elk monitoring in the Elk Valley will estimate population trend and distribution Multi species inventories in the Elk Valley will estimate population density and trend for large mammals Grizzly bear monitoring in the Flathead and Elk Valley will estimate population density and trend Moose inventories in the Flathead and Wigwam will estimate population density (2016 17) and possibly trend (if areas are resurveyed) Mule deer monitoring will estimate population trend and distribution Wolverine monitoring will estimate population density WOLF POPULATION DENSITIES Wolf population densities can be estimated using remote cameras since packs are uniquely identifiable by coloration. A basic analysis will involve the following steps: Determine the number of wolves in each pack for each year (late winter) from photos Determine the proportion of each wolf pack s territory within the study area, from detections and reported observations at cameras within the study area, and outside of the study area For each pack, divide the number of wolves by the proportion of the territory within the study area, and sum the results for all packs The Rocky Mountain National Parks are exploring methods for estimating wolf densities from remote camera data using spatial capture recapture/n mixture models (Jesse Whittington, Wildlife Ecologist, Parks Canada, personal communication). If methods are effective, we will apply them to our study area as well. During this project we will also ask the public to submit 1) information on wolf observations (groups of 2+ wolves) and 2) wolf photos from personal game cameras. For these reports we will gather information on location, date and time of observation/photo, pack size (including adults versus young if known), and pack coloration. 14
GRIZZLY BEAR POPULATION DENSITY Current grizzly bear monitoring in the study area also provides an opportunity to compare results from independent data sources. When classifying grizzly bear photos, we will record whether bears have a radio collar or ear tag, and classify animals as male, female or cub. Currently there are 8 radio collared bears in the Flathead and 10 in the Elk Valley, with plans to increase the sample size to 35. There are also approximately 100 ear tagged bears. We will determine encounter rates for marked (radio collared/ear tagged) and unmarked bears at remote cameras, and analyse data using spatial mark recapture to estimate population size. We will build on methods that are currently being refined by Parks Canada based on peer review (Jesse Whittington, Wildlife Ecologist, Parks Canada, personal communication). Results from this analysis will be compared to estimates from a DNA mark recapture project currently underway in the Elk Valley and Flathead (Garth Mowat, FLNRO, personal communication). We will also have the opportunity to compare cub production (i.e., cubs per female) data from remote cameras to data collected during monthly fixed wing flights to observe radio collared bears (Garth Mowat, FLNRO, personal communication). 15
RESULTS AND DISCUSSION FLATHEAD WIGWAM 1394 events were captured on 28 cameras between July 20 and November 18, 2016, including people and domestic animals. One camera was lost and one was pulled from its location. 1249 of those events were of wildlife. In total, 2450 animals were counted in these events (note that these are not individuals, that same individual may pass by the camera and be classified as a separate event). White tailed deer had the most events (361, with 581 animals), with mule deer second (198, 415 animals), followed by moose (147 events, 202 animals), elk (98 events, but 209 animals). Table 2. Number of events, total animals, and number of cameras that captured 12 ungulate and carnivore species. Note that the events and total animals do not represent individuals of that species since the same individual animal may be photographed more than once. Group Species Events Total Animals # of Cameras % of Cameras White tailed deer 361 581 22 75% Ungulates Mule deer 198 415 18 62% Elk 98 209 17 59% Moose 147 202 18 62% Black Bear 30 30 14 48% Grizzly Bear 58 65 16 55% Cougar 11 11 6 21% Carnivores Lynx 41 41 7 24% Red Fox 9 9 2 7% Bobcat 3 3 2 7% Coyote 17 19 8 28% Wolf 40 86 16 55% 16
(a) # events per camera 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 White tailed Deer Mule Deer Elk Moose (b) # events per camera 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 White tailed Deer Mule Deer Elk Moose (c) # events per camera 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 White tailed Deer Mule Deer Elk Moose Figure 6. Number of events captured of different ungulate species per camera on (a) human trails (n=5), (b) road beds, and (c) wildlife trails. 17
(a) # events per camera 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Black Bear Grizzly Bear Bobcat Cougar Lynx Coyote Wolf Red Fox (b) # events per camera 6.0 5.0 4.0 3.0 2.0 1.0 0.0 Black Bear Grizzly Bear Bobcat Cougar Lynx Coyote Wolf Red Fox 6.0 (c) # events per camera 5.0 4.0 3.0 2.0 1.0 0.0 Black Bear Grizzly Bear Bobcat Cougar Lynx Coyote Wolf Red Fox Figure 7. Number of events captured of different carnivore species per camera on (a) human trails (n=5), (b) road beds, and (c) wildlife trails. 18
ELK VALLEY In 2017, 32 cameras were deployed in the Elk Valley between June 21 and September 5, 2017. These photos have not been sent for analysis yet. Preliminary results will be analyzed in 2018. ONGOING WORK Steenweg et al. (2015) and Obrien (2010) both recommend a sample size of 60 cameras to maximize power to detect trends for grizzly bears using camera based occupancy models. With 29 cameras deployed in 2016, and another 32 in 2017, we will achieve this sample size. However for other species, especially those at lower densities, a larger number of cameras may be required. Therefore we are proposing to deploy an additional 25 cameras in MU 4 22 (Bull River) in 2018. Preliminary results on occupancy will be complete in 2018/19 following two years of data collection. ACKNOWLEDGEMENTS We would like to acknowledge the hard work and advice from FLNRO staff (S. Clow, A. Oestreich, T. Szkorupa, S.Gray, P.Stent, E.Chow, H. Bohm), Parks Canada (R. Steenweg, J. Whittington), and University of Montana (M. Hebblewhite, M. Hessami). A huge thank you to the local volunteers for helping deploy and maintain these cameras (L. Walker, A. Ferguson, M. Jaegli, L. Cook, the Plessis and T. Malish). Additional thanks to the South Rockies Grizzly Bear Project, Elk River Alliance and Sparwood Rod and Gun Club. We would also like to gratefully acknowledge the financial support from the Fish and Wildlife Compensation Program. Dan FLNRO funding 19
Mark s cameras in kind LITERATURE CITED Greenberg, S., and T. Godin. 2012. Timelapse Image Analysis manual. Research report 2012 1028 11, Department of Computer Science, University of Calgary. Alberta, Canada. O Brien, T., J. Baillie, L. Krueger, and M. Cuke. 2010. The Wildlife Picture Index: monitoring top trophic levels. Animal Conservation 13:335 343. Whittington, J., M. Hebblewhite, and R. B. Chandler. 2017. Generalized spatial mark resight models with an application to grizzly bears. Journal of Applied Ecology. Jachmann, H. 2002. Comparison of aerial counts with ground counts for large African herbivores. Journal of Applied Ecology 39:841 852. Moeller, A. K. 2017. New methods to estimate abundance from unmarked populations using remote camera trap data. Page 42. Wildlife Biology. University of Montana, Missoula, MT. APPENDIX 20