Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Hessami, Mateen A.
Master of Science - MSc
University of British Columbia
We tested the hypothesis that: (1) moose reduction and stabilization harvest regimes implemented in the Revelstoke Valley, British Columbia, from 2003-2019 provided a greater cumulative number of moose harvested for people than if the status quo of mostly adult male moose harvesting regimes had persisted amidst background rates of moose habitat decline. Our first hypothesis included two more specific predictions and simulations: (1.2) changes in forest harvesting practices have led to a decrease in optimal seral habitat conditions, thereby reducing carrying capacity (K) for moose: (1.3) moose juvenile recruitment ratios increased amidst moose reduction and stabilization regimes that resulted in increases to per capita resource availability for moose, e.g., a density dependent response. To test these predictions, we first used resource selection functions (RSF) from mostly adult female moose GPS location data (46 individuals) collected between 2004-2019, environmental and anthropogenic data variables, and Generalized Additive Mixed Models (GAMMs) to understand the probability of use of cutblocks by moose as a function of years since cut. We used a previously estimated K of moose abundance in the study area and the results from our RSF GAMMs to forecast the annual K of moose from 2019-2040 under various simulated forest harvest scenarios. Next, we used provincial moose harvest statistics and results from the forecasted K calculations to forecast moose abundance and total moose harvested under simulated forestry and moose harvesting policies. Our RSF GAMM results showed that moose strongly selected middle aged (optimal) cutblocks (10-30 years since cut) and avoided both early seral (0-9 years since cut) and older cutblocks (>30 years since cut) across seasons. We found no effect of year on the availability of optimal aged cutblocks on moose home ranges — suggesting no behavioural changes occurred regarding moose habitat selection for cutblocks through high and low moose density years. We also found that had status quo moose harvest rates (~2.5% annual take, mostly adult males) continued until 2019, 33% less, or approximately 303 fewer moose, would have been harvested and consumed by moose harvesters.
Moose (Alces alces) Behavioural and Population Ecology in the Revelstoke Valley, British Columbia by Mateen A. Hessami BSc., Wildlife Biology, University of Montana, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2022 © Mateen Hessami, 2022 ii The following individuals certify that they have read and recommend to the College of Graduate Studies for acceptance, the thesis/dissertation entitled: Moose (Alces alces) Behavioural and Population Ecology in the Revelstoke Valley, British Columbia. submitted by Mateen Alexander Hessami in partial fulfillment of the requirements of the degree of Master of Science. Dr. Adam T. Ford, Biology Department, Irving K. Barber Faculty of Science, University of British Columbia, Okanagan ——————————————————————————————————— Supervisor Dr. Jesse N. Popp, Indigenous Environmental Science, University of Guelph ——————————————————————————————————— Supervisor Committee Member Dr. Robert Serrouya, Alberta Biodiversity Monitoring Institute ——————————————————————————————————— Supervisor Committee Member Dr. Robert Lalonde, Biology Department, Irving K. Barber Faculty of Science, University of British Columbia, Okanagan ——————————————————————————————————— Supervisor Committee Member Dr. Kevin Hanna, Earth, Environmental, and Geographic Sciences, University of British Columbia, Okanagan ——————————————————————————————————— University Examiner iii Abstract The recovery of an endangered species does not occur in isolation from the peoples who directly share the landscape and food-web with the imperiled species. A fitting example of this concept is the mountain caribou (Rangifer tarandus), where forest disturbance has led to more moose (Alces alces) and wolves (Canis lupus), which in turn has caused caribou to decline to an endangered species status. Here, we tested the hypothesis that: (1) moose reduction and stabilization harvest regimes implemented in the Revelstoke Valley, British Columbia, from 2003-2019 provided a greater cumulative number of moose harvested for people than if the status quo of mostly adult male moose harvesting regimes had persisted amidst background rates of moose habitat decline. Our first hypothesis included two more specific predictions and simulations: (1.2) changes in forest harvesting practices have led to a decrease in optimal seral habitat conditions, thereby reducing carrying capacity (K) for moose: (1.3) moose juvenile recruitment ratios increased amidst moose reduction and stabilization regimes that resulted in increases to per capita resource availability for moose, e.g., a density dependent response. To test these predictions, we first used resource selection functions (RSF) from mostly adult female moose GPS location data (46 individuals) collected between 2004-2019, environmental and anthropogenic data variables, and Generalized Additive Mixed Models (GAMMs) to understand the probability of use of cutblocks by moose as a function of years since cut. We used a previously estimated K of moose abundance in the study area and the results from our RSF GAMMs to forecast the annual K of moose from 2019-2040 under various simulated forest harvest scenarios. Next, we used provincial moose harvest statistics and results from the forecasted K calculations to forecast moose abundance and total moose harvested under simulated forestry and moose harvesting policies. To test if density dependent processes were iv driving high recruitment ratios and population growth rebounds after moose population reduction, we plotted observed moose vital rate estimates against an ecological factor, hectares of optimal seral habitat (e.g., carrying capacity). Our RSF GAMM results showed that moose strongly selected middle aged (optimal) cutblocks (10-30 years since cut) and avoided both early seral (0-9 years since cut) and older cutblocks (>30 years since cut) across seasons. We found no effect of year on the availability of optimal aged cutblocks on moose home ranges — suggesting no behavioural changes occurred regarding moose habitat selection for cutblocks through high and low moose density years, therefore alluding to a density-dependent response due to intraspecific competitive release. We also found that had status quo moose harvest rates (~2.5% annual take, mostly adult males) continued until 2019, 33% less, or approximately 303 fewer moose, would have been harvested and consumed by moose harvesters. As a result of the observed moose reduction and stabilization harvesting regimes, the Revelstoke Valley population of moose did not act as a doomed surplus that would have further buoyed wolf populations for additional years, but rather aided in caribou recovery while facilitating biomass opportunities for moose harvesters. v Lay Summary My thesis investigated a population of moose in the Revelstoke Valley, British Columbia, that have been subjected to population reduction and stabilization. Since 2003, moose have been reduced (2003-2005) and then stabilized (2006-2019) at specific population targets in the Revelstoke Valley for the purpose of keeping wolf densities, and therefore predation on endangered mountain caribou, low. Non-Indigenous, licensed hunters and guide outfitters have carried out moose management in the Revelstoke Valley via regulated harvest of mostly adult females. My central hypothesis was that moose reduction and stabilization harvest regimes implemented in the Revelstoke Valley, British Columbia, from 2003-2019 provided a greater cumulative number of moose harvested for people than if the “status quo”, of mostly adult male moose harvesting regimes had persisted amidst background rates of moose habitat decline. To unpack my central hypothesis, I first investigated what age of disturbed habitat, such as harvested areas in a forest (i.e., cutblock) moose select and use. I then tested the effect of environmental and human variables on moose habitat selection and hunting success for moose harvesters. Lastly, I examined why the ratio of ~8-month-old moose to adult female moose increased so substantially after moose population reduction and stabilization harvest regimes were implemented. I found that if no moose reduction policies had been enacted in 2003 and a status quo harvest policy of allowing a small (2.5% annual take) percentage of the population to be harvested each year (mostly males) continued through 2019 — there would have been 33% less moose harvested by hunters over that 16-year time span. I therefore demonstrated moose reduction and stabilization provided win-win benefits to caribou recovery and some moose harvesters. Additionally, I found that moose preferred cutblocks that are aged between 10 and 30 years since being cut. Lastly, I found that ~8-month-old: adult female moose ratios increased vi because of the sudden increase in resources (e.g., food) available to moose — allowing adult female moose the opportunity to more successfully raise their young due to greater food availability per moose and therefore backfill the moose population while it was being suppressed. vii Preface This thesis used moose GPS collar data spanning 2004-2019 from the Caribou Monitoring Unit (CMU), and the British Columbia government. Anthropogenic and environmental sensing data was sourced from the British Columbia government’s open-source data sharing platforms. Data spanned 1961-2019 and encompassed cutblock, vegetation, road network, weather, moose hunter harvest surveys, and topographical data. Early conversations with Dr. Robert Serrouya and Dr. Bruce McLellan shaped this masters research project which was supported and supervised under Dr. Adam Ford at the University of British Columbia – Okanagan (UBCO). Dr. Jesse Popp provided Indigenous consultation and knowledge sharing oversight as well as thesis review. Dr. Robert Lalonde provided ecology insight, thesis writing space at UBCO, and thesis review. This research was made possible by the UBCO Indigenous Student Cohort Fund, the UBCO Aboriginal Student Award, Dr. Adam Ford’s New Frontiers Research Fund, Irving K. Barber Indigenous scholarship, Wyandotte Nation higher education scholarship, and the Yellowstone to Yukon Conservation initiative’s Sarah Baker memorial grant fund. Resource Selection Function (RSF) models and Generalized Additive Mixed Models (GAMMs) were created by Dr. Adam Ford and me. I created all spatial layers for input into my models and ran all analyses with extensive feedback from my supervisor and supervisory committee. I have ensured that all scripts from my analyses are accessible on the OSF platform (https://osf.io/nkhg9/), and where applicable, have prepared ReadMe files for instructions. viii This manuscript was improved based on feedback from my supervisory committee and will be adapted and submitted as a paper co-authored by Dr. Adam Ford, Dr. Robert Serrouya, and others who have or will directly contribute to its publication. Because of the collaborative nature of this thesis and the subsequent transition of it to publication, I use “we” instead of “I” hereafter. ix Table of Contents Abstract ..................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vii List of Tables ............................................................................................................................ xi Acknowledgments ................................................................................................................... xv Dedication .............................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................. 1 1.1 Introduction ......................................................................................................................... 1 1.1.1 Species Overview ........................................................................................................... 1 1.1.2 Moose in British Columbia ............................................................................................ 3 1.2 Moose and Forestry ............................................................................................................ 3 1.2.1 Endangered Woodland Caribou, Moose, Food Webs .................................................... 4 1.2.2 Moose Reduction and Stabilization ............................................................................... 5 1.2.3 Moose Reduction and Stabilization in the Revelstoke Valley, British Columbia ......... 6 1.2.4 The Future of Moose Reduction and Stabilization ........................................................ 7 1.3 Indigenous Peoples of the Revelstoke Valley, British Columbia .................................... 8 1.3.1 Land-use and Wildlife Management Systems ............................................................... 8 1.4 Questions and hypotheses .................................................................................................. 9 1.4.1 Thesis Questions .......................................................................................................... 11 Question 1 - Harvest: How has 16 years of experimental moose reduction and stabilization influenced the total number of moose harvested compared to alternative scenario in which a status quo harvest regime had continued? ........................................ 11 Question 2 - Habitat: What age of cutblocks (e.g., year since cut) do moose select? ...... 11 Question 3 - Demography: Has a density dependent response influenced increased calf:cow ratios observed in the moose population after population reduction and stabilization? ..................................................................................................................... 11 1.4.2 Conceptual Models of Predictions Evaluated in this Study ......................................... 11 Chapter 2: Moose Behavioural and Population Ecology in the Revelstoke Valley .................. 13 2.1 Methods .............................................................................................................................. 16 2.1.1 Study Area ................................................................................................................... 16 2.1.2 Moose Population Estimates ........................................................................................ 18 2.2 Moose Habitat Selection ................................................................................................... 18 2.2.1 Moose GPS Data .......................................................................................................... 18 2.2.2 Defining Seasons ......................................................................................................... 19 x 2.2.3 Defining availability .................................................................................................... 19 2.2.4 Environmental and Anthropogenic Variables .............................................................. 20 2.2.5 Moose Selection of Cutblocks ..................................................................................... 21 2.3 Effects of Landscape Change on Moose Carrying Capacity ........................................ 22 2.3.1 Carrying Capacity (K) .................................................................................................. 22 2.3.2 Projecting Carrying Capacity into the Future .............................................................. 23 2.4 Moose Density Dependent Responses.............................................................................. 23 2.4.1 Density Dependent Recruitment .................................................................................. 23 2.4.2 Moose Home Range Partitioning Amidst Changing Densities .................................... 24 2.4.3 High and Low Moose Densities - Habitat Selection .................................................... 24 2.5 Moose Harvest ................................................................................................................... 25 2.5.1 Limited Entry Hunting (LEH) ..................................................................................... 25 2.5.2 Moose Harvest Data ..................................................................................................... 25 2.5.3 Observed vs. Simulated Moose Harvest ...................................................................... 26 2.5.4 Moose Harvest Model #1 ............................................................................................. 26 2.5.5 Moose Harvest Model #2 ............................................................................................. 27 2.5.6 Projecting Moose Harvest into the Future ................................................................... 27 2.6 Results ................................................................................................................................ 28 2.6.1 Moose Population Dynamics ....................................................................................... 29 2.7 Moose Habitat Selection. .................................................................................................. 31 2.7.1 Moose Selection of Cutblocks ..................................................................................... 31 2.8 Effects of Landscape Change on Moose Carrying Capacity ........................................ 33 2.8.1 Landscape Carrying Capacity ...................................................................................... 33 2.8.2 Projecting Carrying Capacity into the Future .............................................................. 34 2.9 Moose Density Dependent Responses.............................................................................. 36 2.9.1 Density Dependent Recruitment .................................................................................. 36 2.10 Moose Harvest ................................................................................................................. 38 2.10.1 Moose Harvest Data ................................................................................................... 38 2.10.2 Observed Versus Simulated Moose Harvest .............................................................. 40 2.10.3 Projecting Moose Harvest into the Future ................................................................. 41 2.11 Discussion ........................................................................................................................ 43 2.12 Management Recommendations and Conclusion ........................................................ 51 2.12.1 Management recommendations ................................................................................. 51 xi List of Tables Table 1. Aerial stratified random block moose (Alces alces) population estimates and associated calf:cow ratios for the Revelstoke Valley, British Columbia, 1994-2019. Table 2. Five seasons defined for moose resource selection function (RSF) for the Revelstoke Valley, British Columbia, moose (Alces alces) population based on Schneiderman (2018). Table 3. Independent environmental and anthropogenic variables considered for habitat selection and harvest analysis in the Revelstoke Valley, British Columbia, 1962-2019. Data was sourced from open-source British Columbia Government platforms. Table 4. Estimated degrees of freedom, standard error, and p-values for Generalized Additive Mixed Models relating the probability of use by moose (Alces alces) in response to the set smoother — year since cut (e.g., cutblock age) in the Revelstoke Valley, British Columbia, 2004-2019. Table 5. Observed harvest statistics derived from hunter survey data regarding moose harvest (Alces alces) in the Revelstoke Valley, British Columbia, 1992-2019. Table 6. Moose Harvest Model #2: A linear model for moose (Alces alces) harvest, where moose density, hunter effort, and Julian day of the onset of winter were set as predictor variables based on data from the Revelstoke Valley, British Columbia, 2003-2019. Hunter effort and onset of winter were held constant at the 16-year median (99 days) and (276 day), respectively. xii List of Figures Figure 1. Study area map of the Revelstoke Valley, British Columbia, Canada — the traditional territories of the Secwepemc, Ktunaxa, and Okanagan Indigenous peoples. Moose Global Positioning Satellite locations from 2004-2019, Management Units 4-39 and 4-38 separate the west (limited motorized access) and the east sides of the Lake Revelstoke Reservoir. The forestry data encompasses all cutblocks harvested between 1961-2019 within the study area. Mountain caribou population range encompass existing and recently extirpated populations based on Global Positioning Satellite collar data. ........................................................................................ 17 Figure 2. Annual area of forest harvested in the Revelstoke Valley, British Columbia between 1960 and 2019. Forest harvest data was derived from the British Columbia Government open-source website (2021). .................................................................................................................. 29 Figure 3. Changes in moose abundance (N) in the Revelstoke Valley, British Columbia from 1994–2019 (blue line). Two different moose survey methods are presented: (i) aerial survey abundance estimates using a stratified random block (SRB) design (blue squares); (ii) relative change based on aerial counts of moose from a sample of survey blocks is denoted by the blue line (blue dot). Also shown are calves per 100 adult female ratios (red line; z axis). .................. 30 Figure 4. Moose habitat use of cutblocks in the Revelstoke Valley, British Columbia, as a function of years since cut (combined seasons) derived from Resource Selection Functions and Generalized Additive Mixed Model. The smoother intercepts with 0 at 9 and 30 years since cut, indicating that moose avoid cutblocks 0-9 years since cut and >30 years since cut while selecting for cutblocks that are 10-30 years since cut. Because of the paucity of data past 40 years since cut, the upward trend in the curve for older cutblocks should be interpreted with caution. ......... 32 Figure 5. Results of single biological season Generalized Additive Mixed Models showing the relative probability of use for moose given availability for cutblocks as a function of year since cut in the Revelstoke between 2004-2019. > 0 = 𝑠𝑒𝑙𝑒𝑐𝑡 & < 0 = 𝑎𝑣𝑜𝑖𝑑. Because of the paucity of data past year 40, the upward trend in the curve for older cutblocks should be interpreted with caution. .......................................................................................................................................... 33 Figure 6. Area of "optimal" (10-30 years since cut) cutblocks (e.g., carrying capacity) available in the Revelstoke Valley, British Columbia, between 1994-2019. Results from the Resource Selection Function and Generalized Additive Mixed Model showed moose select 10 to 30 (optimal) year old cutblocks (combined season model). We calculated the hectares of optimal xiii moose habitat from observed 1994-2019 forestry data to understand the decline of carrying capacity in the Revelstoke Valley. ................................................................................................ 34 Figure 7. Area of optimal moose habitat (e.g., carrying capacity) forecasted from 2020-2040 based on six different forest harvest scenarios. Scenario 1: 0 hectares cut annually. Scenario 2: average forest harvesting, defined as the mean annual area cut between 2010-2019 (631 hectares annually). Scenario 3: 25% reduction of average forest harvesting. Scenario 4: 50% reduction of average forest harvesting. Scenario 5: 75% reduction of average forest harvesting. Scenario 6: 25% increase of average forest harvesting. ................................................................................... 35 Figure 8. Recruitment (calves per 100 adult females) as a function of per capita resource availability. The positive effect of resource availability on cow:calf ratios support a density dependent response of moose (y=19.51(𝑒0.021𝑥), 𝑟2 = 0.678, p value= 0.013). Recruitment at K is the calf:cow ratio observed in 2003 when the moose population was estimated to be at carrying capacity (22 calves:100 cows). ..................................................................................................... 36 Figure 9. The selection for cutblocks by moose as a function of years since cut. This analysis was performed using a Generalized Additive Mixed Model where observed moose data was the smoother combined all season and was stratified by low (a: 2007-2019) and high (b: 2004-2006) years of moose density. Moose abundance in high years was n=1650-1155. Moose abundance in low years was 730-263-530, for the years 2007, 2014 (lowest moose N), and 2019, respectively........................................................................................................................................................ 37 Figure 10. The proportion of optimal cutblocks available within each moose home range between 2004-2019 in the Revelstoke Valley, British Columbia. ................................................ 38 Figure 11. Moose Kill Per Unit Effort from 1994 to 2019 in the Revelstoke Valley, British Columbia based on hunter survey data. Pre-reduction phase 1994-2002, where status quo, ~2.5% annual harvest of mostly adult males occurred; moose reduction phase 2003-2005 when 70% of the moose population was harvested via antlerless/adult female harvest; and hte moose stabilization phase 2006-2019, where stabilized harvest regimes occurred with the goal of keeping the moose population below a threshold of n=500 individuals. ...................................... 40 Figure 12. Observed (blue line) number of moose harvested in the Revelstoke Valley, British Columbia, from 2003-2019. Simulated total moose harvests (red line) based on the mean pre-reduction (1994-2002) annual moose harvest rate (2.5%) forecasted through 2019. Simulated xiv moose harvests were applied to our projected moose population based on observed changes to landscape carrying capacity. ......................................................................................................... 41 Figure 13. Forecasted cumulative moose (Alces alces) harvested from 2020-2040 under two simulated moose harvest regimes, both harvest regimes were applied to six forest harvest scenarios. In addition, both panels applied simulated harvest regime on simulated moose N projections based on carrying capacity. Moose Harvest Model #1: pre-reduction effort, Julian day of first snowfall, and moose density is held constant. Moose Harvest Model #2: fixed (2.5%) annual proportional harvest. Scenario 1: halt all forest harvesting, 0 hectares cut annually. Scenario 2: average annual hectares cut, defined as the mean annual hectares cut between 2010-2019 (631 annual hectares cut). Scenario 3: 25% reduction of average annual hectares cut. Scenario 4: 50% reduction of average annual hectares cut. Scenario 5: 75% reduction of average annual hectares cut. Scenario 6: 25% increase of average annual hectares cut. ........................... 43 xv Acknowledgments The completion of my master’s thesis was not possible without the tremendous support of my supervisor, committee members, colleagues, project partners, friends, and family. I am particularly appreciative of the patience, support, and confidence my advisor Dr. Adam Ford showed me throughout this 28-month journey. I look forward to continued collaboration and friendship with Dr. Ford throughout my career. I thank Dr. Robert Serrouya for his inspiring and contagious passion for wildlife ecology and caribou conservation — I look forward to continuing to learn from and work with him in my career. I thank Dr. Jesse Popp for her wisdom and knowledge surrounding Indigenous Knowledge and Two-Eyed-Seeing — I look forward to working with her in the future to advance the powerful intersection of Indigenous and Western knowledge. I also wish to thank Dr. Robert Lalonde for his help and support during my MSc; specifically, Dr. Lalonde granting me access to use his resource lab room which allowed me to conduct meetings and finish my thesis. I would like to thank my WiRE lab colleagues for their support and enthusiasm for wildlife conservation. I thank Dr. Clayton Lamb, Melanie Dickie, Peter White, Dr. Ella Bowles, and Laura Balyx for their above-and-beyond support with the many questions I imposed on them. I wish to thank one of my best friends, Jeffery Nishima-Miller for accompanying me on hunting expeditions, discussing current events, and challenging my thinking concerning conservation policy and life in general. My American best friend, Craig Martynn, has also been a rock in my education journey — and I thank him for that. I also thank Jesse Zeman for always being available to chat and for his ‘on-the-ground’ insights and knowledge of the Revelstoke Valley. xvi I wish to thank the Splatsin government for their collaboration and support throughout my MSc, I learned so much about the perspectives, knowledge, and future visions that the Splatsin people have for the land and wildlife that encompass their traditional territory within the Revelstoke Valley. I specifically thank Trina Antoine, Marshall Jones, Robyn Laubman, Cory Bird, and Kayla Gunner. I very much look forward to continuing to learn and collaborate with the Splatsin and other Indigenous communities to advance reconciliation and best practices for the land, wildlife, and people. This project benefited greatly from the contributions and insight from Ministry of Forest Lands Natural Resource Operations and Rural Development (FLNRORD) wildlife biologists. I wish to specifically thank Patrick Stent, Emily Chow, Luke Vander Vennen, Conrad Theisen, TJ Gooliaff, Alexandra Francis, Andrew Walker, and Craig McLean. I thank the organizations and partners that funded my research and that I worked closely with throughout these 27 months, including: Yellowstone to Yukon, Backcountry Hunters and Anglers, and the Aboriginal department and private donors of UBC Okanagan. I specifically thank Dr. Aerin Jacob for her mentorship, inspiration regarding science communication, and amplification of my work throughout my MSc journey. Finally, I thank Amy Conrad, for her perpetual matriarchal support and positivity. I thank my two sisters, Sophia, and Natasha Hessami for their support of my MSc. I also thank my dad, Farshid Hessami for supporting my pursuit of higher education. xvii Dedication This work is dedicated to the citizens of British Columbia who deserve to have habitat and wildlife management decisions made by a plurality of best-available science, knowledge systems, and through holistic consultation and deliberations. 1 Chapter 1: Introduction 1.1 Introduction In the field of wildlife management, researchers and decision makers are interested in what limits and promotes the distribution of species relative to top-down (e.g., predation) and bottom-up (e.g., habitat) processes. A key concept for this thesis is based on the logistical growth model: as population size increase, resources (e.g., food) can become finite, placing greater pressure and limitations on population growth. For example, as animal populations deplete resources, demographic rates such as adult survival and recruitment start to decline due to intraspecific competition for resources — which ultimately hinders population growth rates. The threshold population size based in resource availability is commonly known as “K” or carrying capacity. For my thesis, carrying capacity is based on the landscape (i.e., resource) and is expressed in terms of the of “moose per hectare” of optimal (e.g., K) cutblock habitat. 1.1.1 Species Overview Moose (Alces alces) are a cultural and ecological significant species across much of their pan-boreal range (Persson 2003, Guyot et al. 2006, Lambden et al. 2007). As the largest member of the deer (Cervidae) family, moose provide significant ecosystem services as well as sustenance, recreation, and economical value to Indigenous and non-Indigenous Peoples across Eurasia and North America (Santomauro et al. 2012, Popp 2019). Moose are late immigrants to North America from Eurasia relative to other Cervids, having reached Alaska from Siberia during the late Wisconsin glacial period, 10,000-24,000 years ago (Dugatkin 2009). Moose continued to spread south as late as the early Holocene, less than 10,000 years ago and are continuing to expand south in North America in present day. 2 Over the last two decades in North America, there have been reports of both increasing and decreasing populations of moose (Timmermann and Rodgers 2017, Kuzyk et al. 2018). Various studies have investigated the causes of these changes (Seip 1992, Mueller et al. 2011, Kuzyk 2016) with declines being attributed to predation, habitat degradation, climate change, disease or parasites, and human conflict (e.g., vehicle collisions). Moose population increases and colonization are attributed to landscape disturbance (e.g., forestry, forest fires, and agriculture) less severe winters, and the suppression of predators by people (Nadeau et al. 2017, Harris et al. 2021). Predators play an important role in limiting moose populations (Gasaway et al. 1992, Severud et al. 2019, Harris et al. 2021, Rempel et al. 2021). In unexploited, multi-predator systems, moose abundance is generally at a low-density (e.g., 400 moose per 1000 km!). In areas with either a single large carnivore predator or none, densities normally exceed 400 moose per 1000 km! (Gasaway et al. 1992), Typical predators of moose include grizzly bears (Ursus arctos), black bears (Ursus americanus), wolves (Canis lupus), and cougars (Felis concolor). Both bears and wolves predate heavily on moose calves within the first few months after birth (Patterson et al. 2013, Severud et al. 2019), with wolf and cougar predation continuing through the winter (Ross and Jalkotzy 1996). In systems where wolves are the sole moose predator (e.g., Isle Royle, Michigan), moose make up greater than 90% of the biomass in wolf diet (Vucetich et al. 2011). Cougars also predate on moose especially in southern latitudes of North America (Ross and Jalkotzy 1996, Harris et al. 2021). In southwestern Alberta, cougars were found to be a significant predator of moose, with moose being a substantial portion of their winter diet (Ross and Jalkotzy 1996). Other sources of moose mortality include disease and parasites, vehicle collisions, and human harvest (Danks and Porter 2010, Kuzyk et al. 2018). 3 1.1.2 Moose in British Columbia In British Columbia, the historical range and distribution of moose has changed considerably amidst increasing forestry and predator suppression campaigns occurring in the 20th century (Hatter 1970, Peterson 1995). Prior to 1860, there were limited records of moose in British Columbia’s “southern” (e.g., southern two-thirds of the province) interior ecosystems (Regelin and Franzmann 1998). Hudson Bay records from trappers and local Indigenous Knowledge revealed moose started to rapidly populate the southern interior and coastal regions of British Columbia at the same time forestry activity and wide-spread predator reduction commenced (West 1962, Santomauro et al. 2012). However, since the early 2000’s, moose have declined by 50 – 70% in some parts of the province, whereas other populations have been considered stable or even increasing, such as the Revelstoke Valley moose population (Serrouya et al. 2011, Kuzyk 2016). The British Columbia moose population was most recently estimated in 2014 at 120,000 to 205,000 moose, declining by approximately 27,500 moose from 2011 (Kuzyk et al. 2018). 1.2 Moose and Forestry Disturbance processes to forest ecosystems (e.g., fire and logging) are integral to maintaining forest health as well as facilitating productive large herbivore populations (Schrempp et al. 2019, Kuzyk et al. 2020, Francis et al. 2021). In British Columbia, climax forest stands are primarily ‘disturbed’ or ‘altered’ due to wildfires and anthropogenic events such as cutblock forest harvesting. Changes to climax forests allow for competitive release (i.e., replacement) of early seral plant species (White 2001), known as secondary succession. Many secondary-succession plant species, which generally grow after a disturbance event are palatable deciduous species preferred by moose and other herbivore species (Bunnell et al. 2004). While 4 the literature varies on the temporal consensus of what seral age of cutblocks (i.e., year since cut) moose prefer or avoid for — there is baseline evidence that moose abundance increases from the creation of early seral vegetation (Smith et al. 2010, Kuzyk 2016, Anderson et al. 2018). In fact, out of any Cervid species in North America, and the Western knowledge/literature recorded about it, moose have the widest degree of variation in population response to anthropogenic disturbance. This broad variation is likely a function of moose colonizing habitats for the first time since the last glacial maximum and Western research continuing to emerge concerning their optimal or preferred habitats. 1.2.1 Endangered Woodland Caribou, Moose, Food Webs While moose benefit from specific types of anthropogenic disturbances — the ultimate factor driving steep and rapid declines in boreal and mountain woodland caribou (Rangifer tarandus) populations across Canada is anthropogenic disturbance (Seip 2008, Boutin and Merrill 2016). For caribou, an ecological process known as apparent competition has emerged because of habitat alteration — driven by resource extraction which has transformed old-growth forest habitats to open-canopy, early seral growth plant communities (Wittmer et al. 2007). These early seral conditions provide abundant forage for moose (Alces alces) and deer (Odocoileus spp.), which are generalist herbivores (DeCesare 2012, Seip 1992, Serrouya 2012). As a result, landscape change has buoyed generalist herbivore populations such as moose and deer, leading to an elevated abundance of wolves, which in turn, increases wolf-caribou interactions and accelerates caribou decline (Dickie et al. 2017, Serrouya et al. 2020). Importantly, climate change is the second human-caused factor facilitating apparent competition (Dawe and Boutin 2016). A drying and warming climate has facilitated the expansion of white- 5 tail deer distribution northward, increasing predator density in its wake and therefore wolf-caribou encounters and predation rates (Latham et al. 2011). Consequently, ultimate changes to the land and climate have reduced adult female survival and recruitment for caribou primarily because of the proximate predation by wolves (Hervieux et al. 2013). For this reason, wolf reduction is being used in several areas in Canada to slow the decline of caribou (Hayes et al. 2013). In addition to predator reduction, some jurisdictions are using regulated hunting seasons to reduce the number of moose, with the idea of removing a primary food source (e.g., moose) for wolves (Serrouya et al. 2017). 1.2.2 Moose Reduction and Stabilization The reduction of primary prey species is sometimes used to reduce the negative effects of predator-mediated apparent competition on caribou (Fernández and De Azua 2010, Serrouya et al. 2015). Moose reduction and stabilization programs have been implemented for caribou recovery in two areas in British Columbia: the Parsnip River region (Steenweg 2011) and Revelstoke Valley (Serrouya et al. 2017). Between 2006-2009 in the Parsnip River region of central British Columbia, moose populations were reduced by 40 percent via regulated hunting (Steenweg 2011). Wolves were radiocollared to test the effects of this management strategy on wolf dispersal rates. Annual dispersal rates of wolves increased from 0.11 in 2007 to 0.63 in 2009 suggesting that moose reduction is a viable strategy to reduce local wolf densities in critical caribou habitat. However, the caribou population in the Parsnip region did not appear to increase as a result of increased moose hunting — possibly because the moose reduction strategy was not intensive enough (Serrouya et al. 2019) and hunter success was limited by terrain and access. In the Revelstoke Valley, moose were reduced by 70 percent between 2003-2005 (Serrouya et al. 2011). Wolf dispersal rates were 2.5 times higher in areas with moose reduction compared to an 6 adjacent area with no moose reduction (Serrouya et al. 2017). This dispersal rate suggests moose reduction can indirectly alleviate predation pressure on caribou via lowering wolf densities as well as local wolf abundance. Further, Serrouya et al. (2017) found that following moose reduction, adult survival of caribou increased from 0.78 to 0.88. Finally, moose harvest rates have been declining in the Revelstoke Valley (Serrouya et al. 2015, see figure 3) suggesting either moose populations are declining, or hunter success is diminishing due to reduced forest harvest activity which likely limits human sightability and reduces moose carrying capacity as well as changing weather patterns which affect harvest success (e.g., onset of snowfall). 1.2.3 Moose Reduction and Stabilization in the Revelstoke Valley, British Columbia Western science approaches to monitoring moose demographic information have been recorded since 1994 in the Revelstoke Valley but has been intensively collected since 2003. Four methods of obtaining and supporting population trend estimates have been employed. Stratified Random Block (SRB) census (absolute abundance), SRB relative census (relative abundance), pellet plots (index). Global Positional Satellite (GPS) and Very High Frequency (VHF) collar data serves to buttress SRB estimates by providing “sightability correction factors” while also providing population composition information (e.g., age and sex ratios). In 1994 and 1995, the Revelstoke Valley (6500 km!), moose population was estimated at n=646 and n=865, respectively (Table 1, Figure 3). In 2003, the intensity of surveys and indices were increased in response to the start experimental moose reduction. Serrouya et al. (2011) concluded that if forest harvest was entirely absent from the Revelstoke Valley and only old growth encompassed the study area, the carrying capacity of moose would be n=303 (167– 478; 95% CI). In 2003, the moose population was estimated at n=1650 (1235-2066 90% CI) with a density of 1.58 km! which was predicted to be at carrying capacity by Serrouya et al. (2011). In 2006, following 7 intensive adult female harvest, the Revelstoke Valley moose density was estimated at 0.96/km! with a population estimate of n=1155 (811-1302, 90% CI). Licensed, limited entry hunting (LEH; a lottery), and guide-outfitter permits increased more than 7-fold between 2003 and 2005. Harvest subsequently increased by 5.5 times between 2003 and 2005 (Poole & Serrouya 2007). In 2010, the population was estimated at n=577 moose (349 – 967, 90% CI) which showed a slight decrease from 2007, n=730 (601- 889, 90% CI). In 2014, the population estimate was n=263 (221–351, 90% CI) moose. In 2017, the population was estimated at n=341 individuals, suggesting that moose numbers increased by 30% from 2014. Then, 2018 saw another increase to n=383 moose. In 2019, the moose population was estimated at n=575 moose, adjusting for wolf removal measures which relaxed predation pressure on moose (Messier 1995, Serrouya et al. 2015). 1.2.4 The Future of Moose Reduction and Stabilization While successful in improving caribou growth rates and reducing wolf densities in the Revelstoke Valley, moose reductions are controversial, and this policy may not persist as a caribou recovery tool into the future (Peterson 2021). Many people rely on access to moose for recreation and subsistence hunting (Santomauro et al. 2012). Recent media attention on harvest allocation for “mother and baby” moose — while misleading — suggests that public scrutiny for antlerless harvest regimes could be diminishing (Hosgood 2020). In addition, the habitat conditions that favoured the growth of moose populations may also be declining (Kuzyk et al. 2018) The amount of new seral growth is declining in response to protections for endangered caribou habitat and a broader slowdown in the forestry sector (Seip 2008). For this reason, meeting the demands for moose harvest, in a changing landscape, while keeping moose populations low enough to protect caribou is a nuanced and critical knowledge gap. 8 1.3 Indigenous Peoples of the Revelstoke Valley, British Columbia The temporal footprint of Indigenous Peoples in British Columbia extends to at least ~14,000 years ago (Muckle 2011, Leddy 2017). Within the last ~75 years, moose have expanded their range into the southern, coastal, and interior temperate regions of British Columbia (Darimont et al. 2005, Santomauro et al. 2012). Many Indigenous communities of southern British Columbia do not have a word for moose in their traditional language — alluding to their absence prior to European colonization (Okanagan Nation Alliance: personal communication 2020). This range expansion has elicited new sources of biomass for subsistence take by First Nations and licensed hunters (Hingston et al. 2020) and has changed the trophic dynamics of ecosystems that evolved without moose. 1.3.1 Land-use and Wildlife Management Systems Within the Revelstoke Valley (Management Units 438/439), three Indigenous governments claim traditional territory; the Secwepemc, Ktunaxa, and Okanagan Nation (Native Land Digital 2021). Land-use and wildlife management decision making in the Revelstoke Valley is evolving to a more holistic and equitable model between Indigenous and non-Indigenous governments (Hessami et al. 2021). The political framework and approaches to make decisions for wildlife management in the Revelstoke Valley have largely excluded the perspectives of the Indigenous governments who hold traditional territory in the area. The consequence has been political friction between provincial and First Nations governments, as well as gaps in wildlife monitoring and adaptive management objectives. These disjuncts between Western and Indigenous governments is what constrains the continued relevance and adherence of the North American Model of Wildlife Conservation (NAM) (Serfass et al. 2018). Under the NAM, wildlife are viewed as a public resource, the role 9 of science is central to wildlife management, and consumptive users (i.e., hunters) are centered as stakeholders (Organ et al. 2012, Feldpausch-Parker et al. 2017). These practices are intertwined with a land governance system that generally promotes the ‘Public Trust Doctrine’ and equitable access to ‘public land.’ The “future” NAM has been recently evoked by Hessami et al. (2021) where a new and holistic wildlife conservation model called: Indigenizing the North American Model of Wildlife Conservation (I-NAM) is detailed. The I-NAM withholds the common ground values and successful conservation mechanism of the NAM while abandoning its colonial and narrow-focused language. Active listening, learning, and cooperation guide the seven refreshed tenets within the I-NAM which set precedent for a new era of conservation and wildlife management where enhanced outcomes for nature, society, and culturally significant species like moose and caribou are prioritized. 1.4 Questions and hypotheses Given these knowledge gaps and the urgency of enhanced and holistic moose management in caribou recovery areas, this thesis investigated the ecological and anthropogenic mechanisms that influenced moose harvest success, habitat selection, and population responses to fluctuations in a changing landscape within the Revelstoke Valley, British Columbia. We first tested the “doomed surplus” hypothesis (Question 1) which states the annual hunter harvest of ungulate populations by humans can be “sustainable” (e.g., compensatory) because the animals harvested would likely perish from natural causes such as overwinter mortality/predation and thus hunter harvest, if adequately regulated, does not have additive impacts to the population (Boyce et al. 1999). However, we extended the doomed surplus hypothesis to a macro (16 year, 2003-2019) scale instead of an annual scale. Specifically, we hypothesized that moose reduction 10 and stabilization policies enacted from 2003-2019 in the Revelstoke Valley, when compared to status quo, mostly adult male harvest regime has provided an increased total number of moose to be harvested amidst background rates of moose habitat decline. Next, we tested the landscape change hypothesis (Question 2) relevant to the Revelstoke Valley moose population (Kuzyk and Heard 2014). Landscape change has diverse implications to moose behaviour and demography throughout their pan-boreal range (Oates et al. 2020, Francis et al. 2021, Mumma et al. 2021). To test the hypothesis that landscape change in the Revelstoke Valley was reducing moose carrying capacity, we used data from 46 moose GPS collars as well as forest harvesting data from 2004-2019 to understand how seral age of cutblocks (e.g., year since cut) affects moose habitat selection. Resource Selection Functions (RSF) and Generalized Additive Mixed Models (GAMMs) were used to understand the link between seral age of cutblocks and the probability of use or avoidance by moose across seasons. We then used the results of our RSF and GAMMs to forecast how moose populations may change under different forest harvesting scenarios, e.g., different annual allowable cut regimes into the future. We further examined the role of weather, elevation, and roads on moose hunters harvest success in our forecasted habitat, moose abundance (N), and hunter harvest scenarios. Lastly, to test if changes in per capita resource availability, moose density, or habitat use influenced increased recruitment (calf:cow) ratios, we plotted those factors against each other to decipher if a dependent response was present in the population. We specifically examined the hypothesis that experimental moose population reduction produced a density dependent response (Question 3) by inducing a: i) lower variation in the age of forests selected and used; ii) and higher calf:cow ratios per changing available resources measured in hectares of optimal habitat. 11 We used a combination of relative and absolute stratified random block (SRB) survey data, hunter harvest data, moose Global Positioning Data (GPS) collar data, and forest harvesting records to evaluate all three hypotheses. We forecasted our carrying capacity (hectares of optimal moose habitat) and moose N results into simulated models to best inform decision makers on how to balance the trade-offs of caribou recovery, forestry practices, and sustenance moose hunting into the future. 1.4.1 Thesis Questions Question 1 - Harvest: How has 16 years of experimental moose reduction and stabilization influenced the total number of moose harvested compared to alternative scenario in which a status quo harvest regime had continued? Question 2 - Habitat: What age of cutblocks (e.g., year since cut) do moose select? Question 3 - Demography: Has a density dependent response influenced increased calf:cow ratios observed in the moose population after population reduction and stabilization? 1.4.2 Conceptual Models of Predictions Evaluated in this Study Avoid Carrying capacity (N animals) (A) (B) Time (years) Select Time since cut (years) 12 Predicted relationships to link landscape change to moose abundance and harvest levels in the Revelstoke Valley, British Columbia, showing: (A) moose habitat selection (avoidance is below the gray dashed line, selection is above it) for an optimal-harvest age response (shaded area). (B) Predicted carrying capacity (K) for moose amidst decreasing annual hectares cut. (C) Moose harvested; the red line shows the observed number of moose harvested under the liberalized, adult female hunting regime; the blue line shows the predicted number of moose harvested under a ‘status quo’ regime without adult female harvest. The blue arrow indicates the onset of a managed wolf reduction program in the study area (2017). (D) Predicted calf:cow ratios (green line) under a ‘status quo’ harvest regime, low and declining calf:cow ratios due to higher moose population which result in lower available resources. The dashed blue line is moose abundance. (E) Moose abundance under current management regime of increased adult female harvest (dashed red line) and observed calf:cow ratios (green line) – increasing with higher harvest (lower N). The blue arrow indicates the onset of a managed wolf reduction program in the area. (Kuzyk & Heard 2014, Serrouya et al. 2011, Serrouya et al. 2015, Messier 1985, Mumma et al. 2020) Annual moose harvest (C) Time (D) Time Moose abundance (E) Time Recruitment (calf:cow ratio) Moose abundance Status quo Liberalized Moose Harvest Strategy Recruitment (calf:cow ratio) 13 Chapter 2: Moose Behavioural and Population Ecology in the Revelstoke Valley Globally, extinction rates of wildlife species are accelerating amidst anthropogenic change and are 1,000 times higher than natural background rates (Pimm et al. 2014, Maheshwari 2021). The causes of these extinctions are widely accepted to include habitat alteration, climate change, overharvest, and the spread of invasive species (Tittensor et al. 2014, Ripple et al. 2017, Tilman et al. 2017). Conservation interventions to address species endangerment vary widely, from establishing protected areas (Deguise and Kerr 2006) to captive breeding programs (Shepherdson 1994), to food-web level management strategies (Serrouya et al. 2019). Less appreciated is the role of community ecology and species interactions in endangered species conservation. Consequently, the prevention of species extinction may require a deeper understanding of how people affect ecological communities as well as the direct role people play in food-web interactions. Competition and predation are two process that structure food-webs and ecological communities (Lawton and Hassell 1981) with consequences for conservation. Competition is often considered an outcome of resource limitation (e.g., scramble competition) or direct interactions, such as interference competition (Isbell 1991, Cohen et al. 1993, Menge 1995). In addition to resource competition, apparent competition also affects food web dynamics (Sinclair et al. 2006). Apparent competition is an indirect density mediated interaction where the abundance of two or more prey species are influenced by a shared predator (Holt 1977, Shapira et al. 2008, DeCesare et al. 2010). Apparent competition has been increasingly linked to declines of prey species across taxa, including the Vancouver Island marmot (Bryant and Page 2005), Sierra Nevada bighorn sheep (Gibson 2006), and the desert tortoise (Kristan III et al. 2004). Moreover, apparent competition 14 has been identified as a barrier to endangered species recovery. Management actions to address apparent competition may require manipulation of bottom-up and top-down processes (Seip 2008, Serrouya et al. 2019). For example, California’s endangered Channel Island fox (Urocyon littoralis) were threatened by golden eagles (Aquila chrysaetos), whose population was tied to the abundance of invasive feral pigs (Sus scrofa). Feral pigs were eliminated to constrain the primary prey of golden eagles in an effort to reduce their densities and improve the chances of fox persistence (Courchamp et al. 2002). Managing the abundance of a primary prey species can thus help meet the recovery targets of rare and endangered species, though unintended outcomes can also occur (Wittmer et al. 2007).The decline and extirpation of woodland caribou (Rangifer tarandus) across North America is also attributed to apparent competition (Seip 2008, Boutin & Merrill 2016, Serrouya et al. 2020). For caribou, apparent competition has emerged because of habitat alteration and climate change (DeCesare et al. 2010, Dawe and Boutin 2016). Habitat alternation has been driven by resource extraction which has transformed old-growth forest habitats to open-canopy, early seral growth plant communities (Wittmer et al. 2007). These early seral conditions provide abundant forage for moose (Alces alces) and whitetail deer (Odocoileus virginianus) (Seip 1992, Hervieux et al. 2013). Whitetail deer have also moved northward in latitude in recent decades due to a warming climate (Dawe and Boutin 2016). As a result, human-induced landscape and climate change has buoyed moose and deer populations, leading to an elevated abundance of wolves (Canis lupus) — which in turn, increases wolf-caribou interactions and accelerates caribou decline (Seip 2008, Boutin and Merrill 2016, Dickie et al. 2017). To address wolf-moose-caribou apparent competition, wolf reduction has been employed across North America (Hayes et al. 2013). In some areas, wolf reduction is direct via lethal 15 removal (Hervieux et al. 2014). In other areas, wolf reduction is indirect via increased harvest of moose by people (Serrouya et al. 2017). While moose reduction and stabilization policies have been successful in reducing wolf densities and increasing caribou populations, in conjunction with other levers (Serrouya et al. 2019), it has been challenged by Indigenous and non-Indigenous hunters. Hunters have voiced opposition to moose reduction strategies because of fears of decreased moose harvest opportunity, cultural practices that do not permit hunting of adult females or young, and shifting social and ecological baselines (Boertje et al. 2010, Brinkman 2018). In this study, we investigated moose hunter harvest, behaviour, and population ecology within the Revelstoke Valley, British Columbia. The Revelstoke Valley moose population has been subjected to landscape and food-web level manipulations for the purpose of recovering the endangered Columbia North and adjacent caribou subpopulations. We tested the broad hypothesis that: (1) moose reduction and stabilization harvesting policies, when compared to status quo moose harvesting policies, have provided an increased total number of moose harvested amidst background rates of moose habitat decline. Our broad hypothesis included two more specific hypothesis, each conditional on the previous one: (1.2) changes in forest harvesting practices have led to a decrease in optimal seral habitat conditions, thereby reducing carrying capacity for moose (Schrempp et al. 2019, Mumma et al. 2021): (1.3) moose population reduction and stabilization produced a demographic density dependent response: i) lower variation in the age of forests selected and used; ii) and higher recruitment (calf:cow) ratios per available resources. Both density dependent responses are the result of higher per capita resource/space availability due to reduced moose populations. We used a combination of relative and absolute stratified random block (SRB) survey data, hunter harvest data, moose Global 16 Positioning Data (GPS) collar data, environmental variables, and forest harvesting records to evaluate these hypotheses. We forecasted our moose carrying capacity (hectares of optimal moose habitat) results into simulated forest harvest models to best inform decision makers on how to balance the trade-offs of caribou recovery, forestry practices, and sustenance moose hunting into the future. 2.1 Methods 2.1.1 Study Area The study area was located within the Columbia Mountains in the Revelstoke Valley of south-central British Columbia, Canada. The study area encompasses the traditional territories of the Secwepemc, Ktunaxa, and Okanagan Indigenous peoples. The Revelstoke Valley study area is 6500 km! and encompasses management unit (MU) 4-38/4-39 (Figure 1), with elevation gradients ranging from 600 meters to 3,400 meters. In 1983, a portion of the Columbia River was dammed, flooding 114 km! of low-elevation forest and created the Lake Revelstoke Reservoir. The terrain is rugged, steep, and motorized access is limited — especially on the west side of the Lake Revelstoke Reservoir (4-39) which is only accessible via watercraft. The study area is defined as an interior temperate rainforest and is comprised of the wet-cool Interior-Cedar-Hemlock (ICH) biogeoclimatic ecosystem classification (BEC) zone (Meidinger and Pojar 1991) as well as Engelman spruce and Subalpine fir (ESSF) BEC zone. Habitat classifications in the study area included riparian sites, old-growth (e.g., never cut), low elevation forest stands (below 1,300 meters) and regenerating cutblocks. Below approximately 1,300 meters, within the ICH BEC zone, western redcedar (Thuja plicata) and western hemlock (Tsuga heterophylla) are the typical climax tree species, whereas above this elevation Engelman spruce (Picea engelmannni) and sub-alpine fir (Abies lasiocarpa) occur. Significant snowfall (>20 meters per year) is typical in the central portion of the Columbia Mountain range at mid elevations (1,800 meters) where 17 caribou spend most of the winter. In valley bottoms (400–500 meters above sea level) snowfall averages ~400 centimeters per year, which is where most other ungulates and their predators spend the winter, including moose and wolves. As the snow melts in the summer, moose, and deer, along with wolves, bears (Ursus spp.), and cougars (Puma concolor) disperse in the mountains. Figure 1. Study area map of the Revelstoke Valley, British Columbia, Canada — the traditional territories of the Secwepemc, Ktunaxa, and Okanagan Indigenous peoples. Moose Global Positioning Satellite locations from 2004-2019, Management Units 4-39 and 4-38 separate the west (limited motorized access) and the east sides of the Lake Revelstoke Reservoir. The forestry data encompasses all cutblocks harvested between 1961-2019 within the study area. Mountain caribou population range encompass existing and recently extirpated populations based on Global Positioning Satellite collar data. 18 2.1.2 Moose Population Estimates Stratified random block surveys have provided relative and absolute (census) moose abundance estimates in the Revelstoke Valley. Stratified random block surveys (SRB) are a method of surveying moose populations and providing robust estimates of moose abundance and population composition, including calf:cow and bull:cow ratios (Gasaway et al. 1986), they are done via helicopter or fixed-wing aircraft in winter months when moose are concentrated and most visible. For the Revelstoke Valley, there have been ten SRB surveys conducted between 1994 and 2019 (Table 1). Further details on provincial aerial moose survey protocols are described in Serrouya et al. (2011). Table 1. Aerial stratified random block moose (Alces alces) population estimates and associated calf:cow ratios for the Revelstoke Valley, British Columbia, 1994-2019. Year Moose Abundance (N) Moose calf: 100 cows 1994 646 69 1995 865 35 2003 1650 22 2006 1155 25 2007 730 27 2010 577 32 2014 263 36 2017 341 45 2018 383 64 2019 530 45 2.2 Moose Habitat Selection 2.2.1 Moose GPS Data To understand behavioural and demographic processes in the Revelstoke Valley moose population, we used GPS collar data from 46 moose that were captured between 2004-2019. Moose were captured by aerial net-gunning and then collared in the month of March when snowpack was deep, and animals could be easily seen. Moose captures followed strict animal care protocols for the Province of British Columbia and the University of Alberta (permit # 19 VI08-49757, and 690905, 2004-09D, 2005-19D). A combination of primarily adult female (n=42) and male (n=4) moose were collared. Fix rates for moose GPS collars ranged from 1 to 16 locations/day. 2.2.2 Defining Seasons To account for seasonal differences in moose behaviour, we divided our analyses into five seasons (Table 2) based on moose home-range analysis conducted in the interior plateau of British Columbia by Scheideman (2018). They included: Late Winter (Jan 15 - Apr 25) when movement rates are lowest. Calving (Apr 26 – Jun 20) when parturient females became solitary, and the onset of plant green-up occurs. Summer (Jun 21 - Sept 12) from plant green-up through peak vegetation biomass to the start of plant senescence. Fall (Sept 13 - Nov. 20) when senescence of vegetation occurs, and male and female moose form mixed sex groups to breed. Lastly, early Winter (Nov 21 – Jan 14), when males and females change movement patterns to conserve energy and moose distribution is concentrated to the valley bottom. Table 2. Five seasons defined for moose resource selection function (RSF) for the Revelstoke Valley, British Columbia, moose (Alces alces) population based on Schneiderman (2018). Season Date Ranges Number of Days Late Winter Jan 15 – Apr 25 101 Calving Apr 26 – Jun 20 56 Summer June 31 – Sept 12 84 Fall Sept 13 – Nov 20 69 Early Winter Nov 21 – Jan 14 55 2.2.3 Defining availability We calculated home ranges with moose GPS locations across seasons using 95% minimum convex polygons (MCP). We calculated MCP’s with the adehabtiatHR package (Calenge 2021) in program R (RStudio Team 2021). Only individual moose with >10 locations 20 per season were incorporated into the Resource Selection Function (RSF) analysis. We used Generalized Additive Mixed Models (GAMMs) to understand individual level habitat selection of cutblocks and how environmental and anthropogenic variables influence habitat selection (Table 3). Table 3. Independent environmental and anthropogenic variables considered for habitat selection and harvest analysis in the Revelstoke Valley, British Columbia, 1962-2019. Data was sourced from open-source British Columbia Government platforms. Covariate Name Description Environmental DEM Digital elevation model VRI Vegetation composite polygons layer that defines landcover type (treed, non-treed, and Biogeoclimatic zones) Snow depth Snow water equivalent transformed to millimeters of snow Onset of Winter Julian day where snow depth > 2.54 centimeters after October 1st MooseDens High moose density years (>0.08 moose per hectare of optimal habitat) and low moose density years (<0.08 moose per hectare of optimal habitat) Anthropogenic D2road Distance to nearest road (primary and forestry roads) Cutage Annualized cutblock raster containing the age of each cutblock for each used (1’s) and available (0’s) moose location 2.2.4 Environmental and Anthropogenic Variables We acquired environmental and anthropogenic spatial layers available through open-source government websites (British Columbia Government 2021). Environmental spatial layers included treed and non-treed landcover rasters, snow depth data, a digital elevation model (DEM) raster, annualized cutblock rasters, and the Normalized Burn Ratio index. Starting at the bottom, landcover types were characterized in accordance with the vegetation composite 21 polygons layer (VRI, British Columbia Government 2021). We extracted two sets of VRI landcover rasters; the first raster was the treed-class raster which encompassed BEC zone designation (either ICH or ESSF) within forested areas in the study area. The second VRI landcover raster was ‘non-treed,’ encompassing any area that was water, open, or bare (e.g., rock). For cutblocks, we created annualized rasters using the Consolidated Cutblock Harvest dataset (British Columbia Government 2021) for each cutblock that coincided with each year of available moose GPS data (2004-2019). Within each annualized cutblock raster we extracted the age of the cutblock (year harvested) for each used (1’s) and available (0’s) moose location occurring within a cutblock. If a used or available point fell within a non-treed rasters (e.g., water, open, bare) that did not overlap with a cutblock, it was not included. Next, we clipped a DEM for the study area (British Columbia Government 2021). Historical snowfall data was obtained from the British Columbia government (2021) Snow Survey Data station located at Mount Revelstoke, British Columbia (1,842 meters). Automated snow stations transmit daily snow water equivalent (SWE) data in millimeters via a Geostationary Operational Environmental Satellite. To convert SWE data into centimeters of snowfall, we used the National Oceanic Atmospheric Administrations (NOAA 2021) conversion ratio of 10:1 SWE (millimeters) to millimeters of snow. For the second anthropogenic spatial layer (after cutblock rasters), we created a raster representing the distance to nearest road from each moose relocation point using the Digital Road Atlas Layers (British Columbia Government 2021). 2.2.5 Moose Selection of Cutblocks To estimate the thresholds of cutblock age (e.g., year since cut) that moose select and use in the study area across seasons, we used Generalized Additive Mixed Models (GAMM) (Pedersen et al. 2019) to fit a non-parametric smoother to cutblock age and a random intercept 22 for factors that included individual animal, season, and year (e.g.,‘burst’; see Online Supporting Information for code). We then used program mcgViz (Fasiolo et al. 2020) in program R (R Core Team, 4.19, 2021) to plot the population-level relative probability of use given availability of cutblocks as a function of years since cut. Following Mumma et al. (2021), we then visually identified thresholds for cutblock age where if moose use was >1 it represented selection and where moose use was <1 it represented avoidance, those thresholds were defined as integers of ‘year since cut.’ Six GAMMs were produced: the first five represented each season (e.g., the factor, Table 2) within the smoother, and the sixth represented combined seasons (e.g., an entire year). The response variable represents the used and available moose locations, for both models. 2.3 Effects of Landscape Change on Moose Carrying Capacity 2.3.1 Carrying Capacity (K) To forecast moose carrying capacity (K) and population abundance (N) to 2040 under differing forest harvesting scenarios (e.g., annual hectares cut), we created a formula based on: 1) the results from our habitat selection analysis defining optimal moose habitat (i.e., year since cut); and 2) the 2003 moose population size, which was estimated to be at ecological carrying capacity (Serrouya et al. 2011). We then divided the moose population size by the amount of optimal habitat on the landscape in 2003 to provide a metric of estimating changes in carrying capacity (e.g., moose per ha) under different scenarios of forest harvest in the future. We assumed that the same value of moose per ha of optimal habitat derived from the 2003 carrying capacity would also apply at lower amounts of optimal habitat. We also assumed that optimal habitat amount determines carrying capacity; however, moose use and obtain nutrition from sub-optimal habitat. Therefore, this assumption means that optimal habitat amount is indexing rather than determining the actual carrying capacity of the study area. 23 2.3.2 Projecting Carrying Capacity into the Future Based on the carrying capacity (i.e., moose per ha) we derived in Section 2.3.1, we used the R package RcppRoll (Ushey 2018) to calculate the total area of each cut block age from 1960-2019. Based on the results of the resource selection function, we binned cut block ages into three categories — recent, optimal, and old habitat — which reflected the selection of cutblocks by GPS-collared moose. We then created six hypothetical scenarios of future forest harvesting and forecasted the study area’s carrying capacity for moose to the year 2040. We assumed all hectares cut derived from forestry records were cutblocks because cutblock forest harvesting is the dominant forestry practice conducted in the Revelstoke Valley. Scenario 1: all forest harvesting stopped in the year 2019 across the study area, we projected ‘0’ annual forest area harvested to 2040. Scenario 2: we took the mean annual forest area harvested within the study area between the years 2010 and 2019, we then projected this mean 20 years into the future. Scenario 3: a 75% reduction in the annual forest area harvested (based on 2010-2019 mean) from 2020 to 2040. Scenario 4: a 50% reduction in the annual forest area harvested (based on 2010-2019 mean) from 2020 to 2040. Scenario 5: a 25% reduction in the annual forest area harvested (based on 2010-2019 mean) from 2020 to 2040. Scenario 6: a 25% increase in the annual forest area harvested (based on 2010-2019 mean) from 2020 to 2040. 2.4 Moose Density Dependent Responses 2.4.1 Density Dependent Recruitment To test if the reduction of the Revelstoke Valley moose population was associated with an increase in calf:cow ratios, we plotted observed calf:cow ratios surveyed between 2003-2019 against changes in per capita resource availability (e.g., hectares of optimal habitat per moose as estimated from the population surveys). Under the density-dependent hypothesis, we expected to see a positive linear relationship between calf:cow ratios and per capita resource availability. 24 2.4.2 Moose Home Range Partitioning Amidst Changing Densities To test if moose behaviour (e.g., partitioning of habitat use based on density) was influencing high recruitment rates and therefore a density dependent response, we plotted observed moose population abundance against hectares of observed ‘optimal’ moose habitat on the landscape. We calculated the cutblock age of all observed moose GPS relocations for each year (2004-2019). We then regressed the frequency of locations per moose home range that occupied an ‘optimal’ aged cutblock on each year using a linear model. A significant and negative slope in this model would suggest that moose home ranges were declining in quality. In this case, we wanted to know if the selected and avoided ages for cutblocks changed with moose density. However, since the observed moose density declined with time, a density dependent response could be confounded with the loss of available optimal aged cutblocks. To address this potential confounding variation, we quantified the proportion of each moose home range that was comprised of optimal aged habitat. 2.4.3 High and Low Moose Densities - Habitat Selection Lastly, we compared moose habitat use (combined seasons) of cutblocks as a function of year since cut for high and low moose densities to test if intraspecific competition influenced habitat use. For example, did the density of moose influence the age classes of optimal cutblocks? To understand this question, we modeled two GAMMs (high and low moose density) where the non-parametric smoother was cutblock age and the response variable was year since cut for both models. High moose density years (>0.08 moose per hectare of optimal habitat) coincided with the years 2004-2006. Low moose density years (<0.08 moose per hectare of optimal habitat) coincided with the years 2008-2019. 25 2.5 Moose Harvest 2.5.1 Limited Entry Hunting (LEH) Our study design was based on a previous ecosystem level perturbation experiment intended to reduce and then stabilize moose populations in the Revelstoke Valley for the purpose of reducing wolf densities to alleviate primary predation pressure on endangered caribou (Serrouya et al. 2011). Starting in 2003, 273 moose (111 adult male, 162 antlerless) hunting permits were allocated based on a lottery system, known as a “limited entry hunt” (LEH) for wildlife management units 4-39 and 4-38 (Figure 1). This was a 77% increase from the 63 moose hunting permits allocated in 2002 (21 adult male, 42 antlerless). Hunting permits were reduced in 2006 to 139 (93 adult male, 46 antlerless) high with ‘antlerless’ (e.g., adult female and calf) permits continuing to be issued through 2019 (288 total, 68 adult males, 220 adult female). Also in 2003, the frequency of moose population surveys increased to monitor the outcomes of the experimental reduction. 2.5.2 Moose Harvest Data We define three eras of moose harvest regimes in the Revelstoke Valley: 1) pre-reduction, 1994-2002; 2) moose reduction (2003-2006); and 3) moose stabilization, 2007-2019. Moose harvest data were obtained by Forest Lands Natural Resources Operations and Rural Development (FLNRORD) harvest statistics collected from voluntary questionnaire surveys sent to licensed hunters each year beginning in 1992. We used harvest statistics from 1994-2019 (as this is when SRB population estimates started), with 17,881 questionnaires and a response rate of 68%. Hunters were asked how many days they spent afield in search of their quarry, which was recorded as ‘effort.’ Hunters were also asked whether they filled their tag, which was recorded as ‘kill.’ We calculated annual (1994-2019) Kill Per Unit Effort (KPUE) in the study area by dividing estimated kills over estimated effort (days), which we refer to herein as “moose 26 harvested.” Due to data inconsistences, we were unable to use mandatory inspection data and guide/outfitter data, yet the overwhelming majority of moose hunters were licensed, or ‘resident hunters’ (Kuzyk et al. 2018). 2.5.3 Observed vs. Simulated Moose Harvest To test our first and central “doomed surplus” hypothesis - that the total moose harvested would have declined for moose harvesters if no moose reduction and stabilization harvest regimes were enacted, we first defined the “status quo” harvesting regime as: the continuation of a low, mostly adult male proportional harvest regime that occurred between 1994 and 2002 (pre-reduction era) in the Revelstoke Valley. To simulate the continuation of a status quo moose harvest regime from 2003-2019, we first calculated the pre-reduction (1994-2002) mean “fixed proportional harvest rate” (e.g., total animals harvested relative to total estimated population) using observed hunter harvest data and SRB population N estimates. To calculate the pre-reduction moose harvest rate, we extrapolated the 1994 (n=658) and 1995 (n=865) pre-reduction population estimates through the next 2003 SRB estimate (n=1650) assuming an exponential population growth rate from 1994-2002. There were years where no aerial population estimates took place (e.g., 1996-2002) and thus we calculated the mean proportional harvest rate by multiplying observed annual kill data against both observed (1994,1995) and simulated (1996-2002) moose populations (N) across pre-reduction years. 2.5.4 Moose Harvest Model #1 We extended the pre-reduction harvest rate (fixed proportion) to the simulated carrying capacity-based moose population estimate from 2003-2019, and 2020-2040 keeping the harvest rate fixed (e.g., constant, at the mean pre-reduction rate). We refer to this moose harvesting scenario as the “Moose harvest model #1.” This fixed proportional harvest strategy allowed us to 27 examine the effects of different moose population sizes on harvest amount based on data available during the pre-reduction era. 2.5.5 Moose Harvest Model #2 We created a linear model for moose harvested, with moose abundance, hunter effort, and Julian day of the onset as predictor variables based on data from the years 2003-2019. We referred to this linear model as the “Moose Harvest Model #2.” We assumed more moose would be harvested if more moose were available and more people were hunting. We included Julian day of the onset of winter because as snow depth increases, moose are known to move from high elevation ‘summer’ range to low elevation areas (Solberg et al. 1999). It is in these low elevation areas where moose are vulnerable to harvest by people since moose usually need to be close to roads for people to harvest them (P.Stent personal communication 2019). To validate this assumption, we calculated the elevation and distance to nearest road as a function of snow depth for all observed moose GPS relations. We found that moose get closer to roads as snow depth increases (Appendix A). For the linear model of moose harvest, we therefore defined an index of the ‘onset of winter’ as the Julian day of first snowfall greater than 2.54 centimeters after October 1st. The 2.54 cm value does not track the snow depth that affects moose movement per se but may reflect the conditions present at higher elevations and for the duration of the hunting season. 2.5.6 Projecting Moose Harvest into the Future We applied the Moose Harvest Model #2 to the six future forestry scenarios (i.e., that predict different landscape carrying capacities), holding effort and onset of winter constant at the median (99 days) and (276 day), respectively. This fixed effort strategy allows us to examine the effects of different moose population sizes on harvest amount based on more nuanced 28 information related to harvest effort and population size that have only become available since the 2003 reduction. We presented the results of both the proportional harvest (Moose Harvest Model #1) and fixed effort (Moose Harvest Model #2), each with the six landscape change scenarios, as potential alternatives that may have come to be had the caribou recovery policies to reduce moose abundance not been enacted. As such, these 12 scenarios were counterfactual projections that illustrate the degree of elasticity in the system to have produced so called ‘win-win-win’ scenarios of moose harvest success (to some moose harvesters), caribou recovery, and forest harvesting. 2.6 Results The annual hectares cut each year in the Revelstoke Valley declined from a peak of 1640 hectares in 1979 to 240 hectares in 2019 (Figure 2). A total of 46 moose were GPS collared between January 2004 and March of 2019, with five animals censored from our analysis due to not having >10 locations per season. The remaining 41 moose used in the RSF analysis included n=39 adult females and n=2 adult males. Our first prediction was supported: that more moose were harvested under the observed harvest regime than if the status quo, pre-reduction harvest rate had persisted. Our second prediction, that moose carrying capacity was declining due to a reduction in early seral disturbance was supported: moose exhibited strong selection for middle aged cutblocks as a function of year since cut (combined seasons) amidst variation in moose density. Our third prediction, that density dependent responses occurred in response to moose reduction as observed by increasing calf:cow ratios was also supported. 29 Figure 2. Annual area of forest harvested in the Revelstoke Valley, British Columbia between 1960 and 2019. Forest harvest data was derived from the British Columbia Government open-source website (2021). 2.6.1 Moose Population Dynamics In 1994 and 1995 the Revelstoke Valley moose population was estimated at n=646 and n=865, respectively (Table 1, Figure 3). In 2003, the intensity of surveys and indices were increased in response to the start experimental moose reduction. Serrouya et al. (2011) concluded that if forest harvesting was entirely absent from the Revelstoke Valley and only old growth encompassed the study area, the K of moose would be n=303 (167– 478; 95% CI). In 2003, the moose population was estimated at n=1650 (1235-2066 90% CI) with a density of 1.58 km! which was predicted to be at carrying capacity by Serrouya et al. (2011). In 2006, following intensive adult female harvest, the Revelstoke Valley moose density was estimated at 0.96/km! with a population estimate of n=1155 (811–1302, 90% CI). Licensed limited entry hunting (LEH; a lottery) and guide-outfitter permits increased more than 7-fold between 2003 and 2005. Harvest subsequently increased by 5.5 times between 2003 and 2005 (Poole & Serrouya 2007). In 2010, the population was estimated at n=577 moose (349 – 967, 90% CI) which showed a 30 slight decrease from 2007, n=730 (601- 889, 90% CI). In 2014, the population estimate was n=263 (221–351, 90% CI) moose. In 2017, the population was estimated at n=341 individuals, suggesting that moose numbers increased by 30% from 2014. Then, 2018 saw another increase to n=383 moose. In 2019, the moose population was estimated at n=575 moose, adjusting for wolf removal measures which relaxed predation pressure on moose (Messier 1995, Serrouya et al. 2015). Figure 3. Changes in moose abundance (N) in the Revelstoke Valley, British Columbia from 1994–2019 (blue line). Two different moose survey methods are presented: (i) aerial survey abundance estimates using a stratified random block (SRB) design (blue squares); (ii) relative change based on aerial counts of moose from a sample of survey blocks is denoted by the blue line (blue dot). Also shown are calves per 100 adult female ratios (red line; z axis). 31 2.7 Moose Habitat Selection. 2.7.1 Moose Selection of Cutblocks Use probability of cutblocks by moose in comparison to availability varied as a function of year since cut. For our combined season GAMM, adult moose avoided ‘recent’ cutblocks (0-9 year since cut), selected for ‘optimal’ cutblocks (10-30 year since cut), and avoided ‘old’ cutblocks (>30 year since cut) (Table 4, Figure 4). This varied seasonally, with the strongest selection occurring in the summer and early winter months (Table 4, Figure 5). Table 4. Estimated degrees of freedom, standard error, and p-values for Generalized Additive Mixed Models relating the probability of use by moose (Alces alces) in response to the set smoother — year since cut (e.g., cutblock age) in the Revelstoke Valley, British Columbia, 2004-2019. Season Estimated degrees of freedom SE P-value Combined seasons 8.08 0.037 <0.0001 Late Winter 6.505 0.108 <0.0001 Calving 7.789 0.063 <0.0001 Summer 6.467 0.065 <0.0001 Fall 6.065 0.079 <0.0001 Early Winter 5.337 0.095 <0.0001 32 Figure 4. Moose habitat use of cutblocks in the Revelstoke Valley, British Columbia, as a function of years since cut (combined seasons) derived from Resource Selection Functions and Generalized Additive Mixed Model. The smoother intercepts with 0 at 9 and 30 years since cut, indicating that moose avoid cutblocks 0-9 years since cut and >30 years since cut while selecting for cutblocks that are 10-30 years since cut. Because of the paucity of data past 40 years since cut, the upward trend in the curve for older cutblocks should be interpreted with caution. 33 Figure 5. Results of single biological season Generalized Additive Mixed Models showing the relative probability of use for moose given availability for cutblocks as a function of year since cut in the Revelstoke between 2004-2019. > 0 = select & < 0 = avoid. Because of the paucity of data past year 40, the upward trend in the curve for older cutblocks should be interpreted with caution. 2.8 Effects of Landscape Change on Moose Carrying Capacity 2.8.1 Landscape Carrying Capacity We divided the estimated moose population of n=1650 in 2003 by the 20,020 hectares of optimal moose habitat identified in our backcasted moose per hectare results. The result of 1,650/20,200 = 0.084 moose per hectare of habitat under carrying capacity conditions. The average annual hectares cut in the Revelstoke Valley between 2010-2019 was 631 hectares, which was used for the forest harvesting scenarios (Figure 6). 34 Figure 6. Area of "optimal" (10-30 years since cut) cutblocks (e.g., carrying capacity) available in the Revelstoke Valley, British Columbia, between 1994-2019. Results from the Resource Selection Function and Generalized Additive Mixed Model showed moose select 10 to 30 (optimal) year old cutblocks (combined season model). We calculated the hectares of optimal moose habitat from observed 1994-2019 forestry data to understand the decline of carrying capacity in the Revelstoke Valley. 2.8.2 Projecting Carrying Capacity into the Future We found an overall decreasing trend of moose carrying capacity when projecting the current average (2010-2019) annual hectares cut into the future. However, if annual hectares cut increased from 2010-2019 levels, carrying capacity of moose could also increase. Scenario 1: Optimal moose habitat was eliminated within the study area by the year 2037 when all new forest harvesting stopped as of 2020 (Figure 7). Scenario 2: optimal moose habitat persisted until 2038 when new cutblocks averaged 631 annual hectares per year until 2040. Scenario 3: optimal moose habitat decreased under a 25% reduction of annual hectares cut over the next twenty years (based on 631 annual hectares cut) but stabilized around 30,980 hectares in 2035. Scenario 4: 35 optimal moose habitat rapidly diminished under a 50% less annual hectares cut regime to 6,056 hectares of optimal habitat by 2035. Scenario 5: optimal moose habitat diminished at a lower rate to 8,142 hectares by 2035 under a 25% reduction in annual hectares cut. Scenario 6: optimal moose habitat stayed relatively constant from what it was between 2010-2019 at 14,000 hectares of optimal moose habitat when annual hectares cut increased by 25% (Figure 7). Figure 7. Area of optimal moose habitat (e.g., carrying capacity) forecasted from 2020-2040 based on six different forest harvest scenarios. Scenario 1: 0 hectares cut annually. Scenario 2: average forest harvesting, defined as the mean annual area cut between 2010-2019 (631 hectares annually). Scenario 3: 25% reduction of average forest harvesting. Scenario 4: 50% reduction of average forest harvesting. Scenario 5: 75% reduction of average forest harvesting. Scenario 6: 25% increase of average forest harvesting. 36 2.9 Moose Density Dependent Responses 2.9.1 Density Dependent Recruitment We found a strong density dependent response when assessing calf:cow ratios against per capita habitat availability (hectares of carrying capacity between 2003-2019) with a regression value of r! = 0.678. The calf:cow ratio in 2003 was 22:100 which was plotted as a linear benchmark for moose per hectare when the population reaches K (Figure 8). Figure 8. Recruitment (calves per 100 adult females) as a function of per capita resource availability. The positive effect of resource availability on cow:calf ratios support a density dependent response of moose (y=19.51(𝑒!.!#$%), r# = 0.678, p value= 0.013). Recruitment at K is the calf:cow ratio observed in 2003 when the moose population was estimated to be at carrying capacity (22 calves:100 cows). 2.9.2 Moose Home Range amidst Changing Densities Moose selection of optimal habitat was similar under different population densities. At low moose densities, optimal habitat was 11-28 years since cut and 13-35 years since cut under 37 high moose densities (Figure 9). Lastly, we observed a stable relationship between background rates of declining habitat amount and the proportion of optimal habitat in each moose’ home range (Figure 10). Figure 9. The selection for cutblocks by moose as a function of years since cut. This analysis was performed using a Generalized Additive Mixed Model where observed moose data was the smoother combined all season and was stratified by low (a: 2007-2019) and high (b: 2004-2006) years of moose density. Moose abundance in high years was n=1650-1155. Moose abundance in low years was 730-263-530, for the years 2007, 2014 (lowest moose N), and 2019, respectively. 38 Figure 10. The proportion of optimal cutblocks available within each moose home range between 2004-2019 in the Revelstoke Valley, British Columbia. 2.10 Moose Harvest 2.10.1 Moose Harvest Data Pre-reduction (1994-2002) Kill Per Unit Effort was variable yet high (0.301). During the moose reduction phase (2003-2005), KPUE began to decline (0.122) because of the increased fixed effort harvest regimes and subsequent high hunter harvest success (Table 5, Figure 11). Lastly, in the moose stabilization phase (2006-2019), mean KPUE was reduced further but remained relatively consistent (0.050). Table 5. Observed harvest statistics derived from hunter survey data regarding moose harvest (Alces alces) in the Revelstoke Valley, British Columbia, 1992-2019. Year Moose harvested Hunter effort (days) 1992 14 43 1993 8 55 1994 16 61 1995 18 45 1996 23 76 39 1997 30 149 1998 27 103 1999 39 96 2000 34 130 2001 51 141 2002 40 171 2003 155 791 2004 240 2342 2005 117 1305 2006 32 320 2007 21 390 2008 25 577 2009 42 1082 2010 20 541 2011 32 711 2012 14 661 2013 14 679 2014 10 309 2015 22 400 2016 17 489 2017 26 749 2018 48.04 722.75 2019 66.55 917.5 40 Figure 11. Moose Kill Per Unit Effort from 1994 to 2019 in the Revelstoke Valley, British Columbia based on hunter survey data. Pre-reduction phase 1994-2002, where status quo, ~2.5% annual harvest of mostly adult males occurred; moose reduction phase 2003-2005 when 70% of the moose population was harvested via antlerless/adult female harvest; and hte moose stabilization phase 2006-2019, where stabilized harvest regimes occurred with the goal of keeping the moose population below a threshold of n=500 individuals. 2.10.2 Observed Versus Simulated Moose Harvest Based on the hunter survey data, 917 moose were harvested from 2003-2019. Under a simulated, status quo harvest regime where 2.5% of the moose population was harvested annually, 604 moose would have been harvested from 2003-2019, a difference of 33% (Figure 11). 41 Figure 12. Observed (blue line) number of moose harvested in the Revelstoke Valley, British Columbia, from 2003-2019. Simulated total moose harvests (red line) based on the mean pre-reduction (1994-2002) annual moose harvest rate (2.5%) forecasted through 2019. Simulated moose harvests were applied to our projected moose population based on observed changes to landscape carrying capacity. 2.10.3 Projecting Moose Harvest into the Future For our forecasted moose harvest simulations beyond 2019, the fixed effort harvest strategy (Figure 14, Panel 1) produced the greatest cumulative moose harvested (n=670) when average forest harvesting practices increased by 25% from 2020-2040. Under a fixed proportional harvest regime of 2.5% annual harvest (Figure 14, Panel 2), n=629 moose were harvested from 2020-2040 (Panel 2) when average forest harvest was increased by 25%. Under a halt forest harvest scenario, n=255 moose were harvested via proportional harvest (Moose Harvest Model #2) from 2020-2040, whereas n=82 moose were harvested under the fixed proportional (Moose Harvest Model #1) regime. A continuation of average forest harvesting practice under a proportional harvest regime would produce n=554 moose for harvesters, 42 whereas n=470 moose for a fixed harvest regime. A 75% reduction in annual forest harvesting based on the 631-hectare AHC would produce n=330 moose for harvesters compared to n=103 for a fixed proportional harvest regime (Figure 13, Moose Harvest Model #2). Table 6. Moose Harvest Model #2: A linear model for moose (Alces alces) harvest, where moose density, hunter effort, and Julian day of the onset of winter were set as predictor variables based on data from the Revelstoke Valley, British Columbia, 2003-2019. Hunter effort and onset of winter were held constant at the 16-year median (99 days) and (276 day), respectively. Coefficient Estimate SE P-value Intercept 228.800 137.800 0.111 Hunter Effort 0.078 0.006 0.000 Moose Density 0.067 0.007 0.000 Snow Depth 0.627 0.474 0.209 43 Figure 13. Forecasted cumulative moose (Alces alces) harvested from 2020-2040 under two simulated moose harvest regimes, both harvest regimes were applied to six forest harvest scenarios. In addition, both panels applied simulated harvest regime on simulated moose N projections based on carrying capacity. Moose Harvest Model #1: pre-reduction effort, Julian day of first snowfall, and moose density is held constant. Moose Harvest Model #2: fixed (2.5%) annual proportional harvest. Scenario 1: halt all forest harvesting, 0 hectares cut annually. Scenario 2: average annual hectares cut, defined as the mean annual hectares cut between 2010-2019 (631 annual hectares cut). Scenario 3: 25% reduction of average annual hectares cut. Scenario 4: 50% reduction of average annual hectares cut. Scenario 5: 75% reduction of average annual hectares cut. Scenario 6: 25% increase of average annual hectares cut. 2.11 Discussion We evaluated how the loss of optimal moose habitat and increased antlerless moose hunting effort contributed to the total number of moose harvested compared to a simulated, status quo harvest regime (~2.5% proportional harvest) (Figure 12). We found optimal moose habitat (e.g., 10–30-year-old cutblocks) declined by 29% between 2003-2019 and will continue to by a rate of 5% annually through 2040 if annual hectares cut in the Revelstoke Valley were to cease to zero beginning in the year 2020 (Figure 7). We also confirmed a density dependent 44 response in the moose population, with high juvenile recruitment rates correlated to an increase in per capita resource availability made possible by experimental moose reduction and stabilization (Figure 8). The results from this thesis suggest that moose reduction and stabilization aimed at caribou recovery can meet or exceed typical moose harvest levels seen under a non-caribou recovery, status quo moose harvesting regime because 33% more moose were harvested under the observed harvest regime in the Revelstoke Valley moose population compared to a simulated, pre-reduction continuation of 2.5% annual proportional harvest (Figure 12). We also evaluated if changes in forestry practices have led to a decrease in optimal habitat conditions for moose, thereby reducing carrying capacity. We found moose avoided early seral, or ‘recent’ cutblocks (e.g., 0-9 year since cut), selected middle aged, ‘optimal’ cutblocks (e.g., 10-30 year since cut), and avoided senescent, ‘old’ cutblocks (>31 year since cut) (Figure 4). We associated the area of optimal moose habitat as landscape carrying capacity for three reasons. First, we observed distinct temporal patterns of probability of use by moose for middle aged cutblocks and avoidance for recent and old cutblocks with our combined season, early winter, and summer GAMMs (Figure 4, 5). Secondly, our high and low density (combined season GAMM) (Figure 9) showed strong use for middle-aged cutblocks despite variation in per capita resource availability amidst fluctuating moose densities. And third, the study areas interior temperate rainforest ecosystem under pristine (e.g., old growth) conditions would not have conducive forage availability/quality for moose (Serrouya et al. 2011). Serrouya et al. (2011) estimated Carrying capacity at n=303 (167-478) under pristine conditions for the Revelstoke Valley. Pristine translates to ’pre settler contact’ (e.g., before ~1900) landscape conditions where 45 an old growth interior temperate rainforest provided limited forage quality for sparse and low density moose populations to persist (Santomauro et al. 2012). Our results were similar to other non-linear responses to year since cut for cutblock habitats found elsewhere in British Columbia. In the interior Bonaparte plateau, an ecosystem that is much drier and colder than the Revelstoke Valley study area, moose probability of use was strongest for regenerating cutblocks; specifically 9-24 years since cut (Mumma et al. 2021). In Newfoundland, moose selected cutblocks aged 8-10 year since cut (Parker and Morton 1978). The proximate factors affecting moose temporal selection of cutblock stands include the rate of forest re-generation, re-planting practices (e.g., tree species planted), and the densities of roads and large carnivores (Bjørneraas et al. 2012, DeCesare et al. 2014, Kuzyk et al. 2018, Francis et al. 2021). Since the 1990s, moose harvest levels have fluctuated dramatically in the Revelstoke Valley, giving rise to three distinct harvest management eras: 1) pre-reduction 1994-2002; 2) moose reduction 2003-2005; 3) and moose stabilization 2006-2019. The pre-reduction mean metric of moose harvest success relative to effort, which we call Kill Per Unit Effort was high (0.30) (Table 2, Figure 4) corresponding to a growing moose population approaching K and low regulated LEH annual harvest rates (~2.5%) of mostly adult males. For context, across 16 hunting districts in Montana, USA, the KPUE for moose harvested between 1983-2014 averaged ~0.078 (DeCesare et al. 2014). During the moose reduction phase in the Revelstoke Valley, KPUE declined (0.122) because of the increased fixed effort harvest regimes and subsequent high hunter success (Table 5, Figure 12). Across the moose stabilization management phase, mean KPUE was reduced further but remained relatively consistent (0.050) across years — suggesting more balanced rates of hunter success was emerging despite high annual rates of 46 mostly adult female moose harvest (Figure 12). This fixed effort harvest system is employed under a “proportional harvesting” strategy (Sæther et al. 2001) and is conducive to moose reduction efforts in other systems (Bergerud et al. 1968). Across all three management phases, moose hunter harvest success was positively affected by the number of hunter days (i.e., effort), moose abundance, and weakly so by the onset of winter relative to the start of the hunting season (Table 6). To moose hunters, snow provides increased sightability (Feit 1987), greater tracking ability (Kelsall 1969), and higher concentrations of moose at lower elevations and near roads (Renecker and Hudson 1986, Stephenson et al. 1998, Anderson et al. 2018). The correlation between winter onset and hunter success is not as pronounced in flatter, less topographically rugged habitats such as the Parsnip region of northeast British Columbia (Steenweg 2011) where a similar moose reduction policy as observed in the Revelstoke Valley was implemented from 2007-2009. Topography and hunter success is one hypothesis as to why the effectiveness of the Parsnip moose population reduction effort was less successful (e.g., 40% reduction of estimated moose population in first two years) when compared to the Revelstoke Valley moose reduction effort (70% reduction of estimated moose population in the first two years). Importantly, Revelstoke Valley moose were ‘pushed’ by deep snow into areas where they were more accessible to hunters, such that KPUE was high even during the stabilization phase. While the moose reduction phase of 2003-2005 coincides with the decline of wolf numbers and the stabilization of caribou populations (Serrouya et al. 2015), the suggestion that moose hunting alone ‘saved’ the Columbia North caribou herd requires deeper scrutiny. First, we observed that hunter harvest between 2003 and 2005 likely contributed to a 70% decline from the estimated moose population in 2003 (n=1650). In 2019, the moose population was estimated 47 at n=575 moose. The population target for the study area was 300 moose, but this is an increasingly challenging target to achieve due to wolf reductions (2017), a later onset of winter due to climate change, and high recruitment rates because of density dependent processes. Increased partnership with Indigenous moose harvesters directing them to difficult to access, yet high-density moose areas in the late is a viable tool to offset low harvest while enhancing food-security opportunity and connection to the land to Indigenous peoples. The experimental reduction of moose in Revelstoke is not a novel approach to ungulate management. Studies have shown strong density dependent responses post population reductions with elk (Van Beest et al. 2014), white-tailed deer (Lashley et al. 2011), and in other populations of moose (Bowyer et al. 1999, Solberg et al. 1999). Despite the aforementioned studies that revealed density dependent responses in ungulate populations as a function of greater available resources post population reduction, there remains an important knowledge gap concerning ungulate harvest science: can suppressing an ungulate population via regulated hunting well below carrying capacity stimulate increased recruitment rates that enhance or maintain biomass for ungulate harvesters (McCullough 1979, Boyce et al. 1999, Bowyer et al. 2020)? Our study contributes to this Western knowledge gap with multiple lines of evidence. For example, the observed time series of estimated moose population and recruitment rates in the Revelstoke Valley varied most substantially from a population size of n=1650 in 2003 at K when calf:cow ratios were observed at 22:100, to a population size of n=383 in 2018 when calf:cow ratios were 64:100. The 42% increase in calf recruitment coincided with a 24.8% reduced area of optimal habitat during those fifteen years. Compared to other moose populations in similar montane, large carnivore (e.g., wolf, grizzly bear, mountain lion) systems, calf:cow ratios exceeding 30:100 is an indicator of a stable or growing moose population (Ballard et al. 1981, Schwartz and 48 Hundertmark 1993, DeCesare et al. 2014). In the Revelstoke Valley, calf:cow ratios peaked at 64:100 following the commencement of aerial wolf reduction, which began in the winter of 2017. The increased calf:cow ratios observed in the Revelstoke Valley post moose reduction align with the “extremely high” recruitment rates observed in the Kalgin Island, Alaska moose population which was heavily reduced from 1982-1986 via hunter harvest to address the population surpassing K (Bowyer et al. 1999). The Kalgin Island moose population exhibited a similar pattern of decline, then increase in KPUE in comparison to the Revelstoke Valley moose population while high recruitment rates backfilled the suppressed adult female population. Density dependent responses for moose are exacerbated when factors such as large carnivore densities, weather, and habitat quality influence the population — as observed in the closed Isle Royale moose population. Weather patterns (e.g., snowfall and temperature) and habitat quality explained more of the interannual variation in moose population growth than predation in the Isle Royle moose population (Vucetich and Peterson 2004). For the Revelstoke moose population, the effect of habitat quality under a moose population surpassing K, was just beginning to be realized as experimental reduction commenced right as the population was estimated to be at K (Serrouya et al. 2011). In Ontario, Brown (2011) showed that density dependent processes in moose populations were more closely attributed to forage availability (e.g., mixed deciduous forests) than moose density and hunter harvest. The findings from Brown (2011) challenges our conclusions that forage availability and moose density is a primary surrogate for density dependent responses, yet we hypothesize this is because the Revelstoke Valley exhibits less deciduous plant availability, and therefore nutritional quality/carrying capacity per hectare when compared to Ontario. 49 As landscape change will continue to affect moose habitat quality and carrying capacity in the Revelstoke Valley, hunter harvest dynamics (e.g., harvest success) will depend on a combination of future forestry activity, weather, and moose harvest regimes. The best case-scenario for moose harvesters in the Revelstoke Valley concerning habitat management is a 25% (or greater) increase in annual hectares cut based on the 2010-2019 mean. Under a 25% increase in forest harvesting scenario, the carrying capacity of moose was n~1300 in the year 2040 — a similar carrying capacity to what was observed in 2011. Importantly, the higher predicted moose densities because of a 25% increase in forest harvesting could increase wolf densities above the 6.5 wolves/1000 km! threshold where wolf-caribou predation would have additive implications to caribou populations (Bergerud and Elliot 1986). The worst-case forest harvesting scenario for moose harvesters was a halt forestry harvest scenario starting in the year 2020, where by 2038, just 254 hectares of optimal moose habitat would exist within the Revelstoke Valley, translating to a K of n~21 moose. We note two limitations in the analyses and subsequent inferences garnered from this thesis. First, we did not directly assess vegetation quality or abundance on the ground (e.g., biomass and plant species identification) as a function of time-since-cut. In other words, we did not perform “ground truthing exercises” to test our moose habitat selection models. Mumma et al. (2021) found that regenerating (10-24 years since cut) cutblocks are preferred by moose, yet the primary predictor of moose probability of use was vegetation structure of cutblocks, not year since cut. The additions of field-based nutritional metrics would provide better information on moose carrying capacity under different forestry harvest scenarios, such as a fecal diet analysis (Felton et al. 2021). Our second limitation of this study was that the moose harvest statistics that were central to our study lacked consistent compulsory inspection (e.g., mandatory) data prior to 50 when moose harvest was initiated (2003). Such harvest data could have improved the extrapolation we performed to calculate pre-reduction harvest rates. Despite these limitations we are still confident in the harvest data we utilized in our analysis and projections, in part because Serrouya et al. (2015) found that harvest data was correlated to population size. Society and wildlife managers have mostly overlooked primary prey reduction as a viable tool to recover endangered prey species due to minimal case studies and dubious stakeholder backing. Moose reduction and stabilization in the Revelstoke Valley has provided enhanced moose hunting opportunity and food security for licensed (non-Indigenous) hunters compared to status quo harvest policies (Figure 9). In addition, moose reduction policies have indirectly reduced wolf densities and contributed to increased population growth rates for the Columbia North caribou population (Serrouya et al. 2017, 2019). Importantly, moose reduction policies can reduce the reliance on wolf reduction, which are often carried out via aerial gunning to aid in proximate caribou recovery actions. Indeed, wolf reductions may be seen as a necessary, short-term tool to reduce predation on endangered caribou (Serrouya et al. 2019), but wolf reduction efforts carry immense social and political pressure to be curtailed and/or removed. Moose reduction policies in the Revelstoke Valley have also been met with controversy and misinformation by both Indigenous and non-Indigenous communities and stakeholders. The evidence presented in our research demonstrate a promising middle ground and potential valuable approach in the complex and costly effort to recovering an endangered species. From an economic standpoint, enlisting Indigenous and licensed hunters to contribute to endangered species recovery via increased antlerless/adult female hunting seasons can alleviate the government (e.g., taxpayer dollars) implementing costly food-web manipulation such as targeted moose reduction (e.g., government contracted moose removal). Licensed hunters instead pay to 51 do so, filling their freezers with ethically sourced high-quality protein and fostering citizen involvement in conservation. ‘Citizen’ involvement in moose management and conservation encompasses Indigenous and non-Indigenous Peoples working together and deciding upon licensed allocations that consider the inherent rights of unlicensed moose harvesters (e.g., Indigenous Peoples). As such, the necessary upshot to our research findings is to weave Indigenous perspectives and peoples into the Revelstoke Valley moose stabilization harvest program to achieve best-available outcomes for caribou recovery while promoting equality and reconciliation. 2.12 Management Recommendations and Conclusion 2.12.1 Management recommendations We recommend wildlife managers interested in improving moose management and sustained yield in caribou recovery systems first prioritize understanding moose habitat selection and use by conducting a habitat selection analysis (e.g., RSF). By first quantifying thresholds of seral stage in disturbed systems that are suitable for moose populations, moose habitat can be projected for future land-use treatments or policies in the interest of maximizing or perhaps reducing moose populations. In other words, a ‘bottom-up,’ habitat first approach is the most effective and efficient pathway to understanding and mediating moose populations. Secondly, we advise decision makers maximize the data and information available to them to inform baseline and experimental moose habitat and population treatments. Lastly, we emphasize moose behavior and population ecology are variable across their geographical range . Therefore, the specific treatments that were successful in recovering caribou and providing enhanced biomass to moose harvesters in the Revelstoke Valley may, and likely will differ elsewhere. Within the Revelstoke Valley (Management Units 438/439), three Indigenous governments claim Traditional Territory; the Secwepemc, Ktunaxa, and Okanagan Nation 52 (native-land.ca: 2019). The unique perspectives, knowledge-systems, and future visions held by the Indigenous Peoples who have inhabited the Revelstoke Valley for thousands of years, must be equitably represented in wildlife and habitat decision making (Hessami et al. 2021). Moose have become a central component of food security for the Secwepemc, Ktunaxa, and Okanagan Nation communities over the last half century. However, in the Revelstoke Valley, the alteration of food webs (moose reduction and stabilization) to save endangered mountain caribou presents several questions for Indigenous communities who have grown accustomed to moose as a primary source of food, such as: 1. Do moose reduction and stabilization strategies impede Indigenous harvest opportunity? 2. Is this caribou recovery strategy an acceptable trade-off to restore caribou populations? These questions must be addressed through knowledge sharing forums and co-management agreements to advance reconciliation and better management/conservation practices for moose, caribou, and the land. Indigenous and Provincial governments tasked with recovering populations of caribou in conjunction with managing moose populations would benefit from understanding the relative importance of caribou recovery levers such as altering moose harvest regimes and annual allowable cut permits. Of value, would be forecasting both annual hectares cut and moose harvest scenarios in which moose density is maintained at specific thresholds for caribou recovery and satisfactory or average (2010-2020) levels of Kill Per Unit Effort for moose hunters. 53 The future of moose management in British Columbia involves co-management and reconciliation efforts between provincial and First Nations governments (British Columbia government 2015, Popp et al. 2019). Initiatives and treaties such as “Together for Wildlife”, the “United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP)”, and “Bill 41” (British Columbia implementing UNDRIP into governance), advance holistic land and wildlife management strategies that are necessary for the conservation of moose and other culturally significant species in British Columbia. These approaches are known as co-management and Two-Eyed seeing (Bartlett et al. 2012) and interweave Indigenous and Western Knowledge systems to progress effective and equitable decision making. The spatial occurrence of imperiled southern mountain caribou populations is prominently tied to First Nations traditional territory; thus, the Western science research explored within this are significant to future co-management approaches to recover endangered caribou populations as well as better manage culturally significant species such as moose. 54 Bibliography Anderson, M., B. N. McLellan, and R. Serrouya. 2018. 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Journal of Animal Ecology 76:568–579. 68 Appendix Appendix A: Effect of Roads, Elevation, Snow Depth, and Julian Day on Moose Habitat Selection We first examined the effect of snow depth and elevation on moose habitat selection to understand how weather could influence hunter harvest success. To do so, we created a GAMM where the observed location points generated from our RSF were inputted as the smoother while snow depth and elevation were inputted as the response variables. Secondly, we examined the effect of Julian day on moose proximity to the nearest primary and secondary roads to understand how the timing of the moose hunting season influences hunter harvest success. Our prediction was that moose hunter harvest success is affected by snow-depth during the hunting season which facilitates behavioural shifts for moose; pushing them to lower elevations, closer to roads, and more vulnerable for harvest. To test this, we created a GAMM where the observed location points generated from our RSF were inputted as the smoother, and distance to nearest road (e.g., primary, and secondary forestry roads) and Julian day inputted as the response variables. We found that as snow depth increases, observed moose location points were closer to primary and secondary roads. Similarly, we found observed moose location points were closer to primary and secondary roads as a function of Julian day. Our results demonstrate that moose behaviour and fidelity to roads is correlated with snow depth and Julian day due to locomotion and forage limitations induced by snow cover on the landscape. Therefore, in the Revelstoke Valley and other relevant mountain systems, the settings of hunting seasons should reflect timing and changes of weather patterns (e.g., climate change). 69 Figure A1. Moose selection (fall season) of primary and secondary roads in the Revelstoke Valley, British Columbia, as a function of snow depth (snow water equivalent (mm)) derived from Resource Selection Functions and Generalized Additive Mixed Models. Moose location data spans 2004-2019. 70 Figure A2. Moose selection (combined season) of primary and secondary roads in the Revelstoke Valley, British Columbia, as a function of Julian Day derived from Resource Selection Functions and Generalized Additive Models.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ford, Adam
Moose (Alces alces) behavioural and population ecology in the Revelstoke Valley, British Columbia
Text