19 WOLF PREDATION ON MOOSE IN NORTH–CENTRAL BRITISH COLUMBIA Morgan Anderson1, Matthew Scheideman1, Shelley Marshall1,*, Dexter Hodder2 1British Columbia Ministry of Water, Land and Resource Stewardship, 2000 South Ospika Boulevard, Prince George, B.C. V2N 4W5, Canada; 2John Prince Research Forest, University of Northern British Columbia, 356 Douglas Avenue, Fort St. James, B.C. V0J 1P0, Canada Corresponding author: Morgan L. Anderson, British Columbia Ministry of Water, Land and Resource Stewardship, 2000 South Ospika Boulevard, Prince George, B.C. V2N 4W5, Canada. 250-649-4392 Email: Morgan.Anderson@gov.bc.ca ABSTRACT: Moose populations declined substantially following widespread salvage logging of moun- tain pine beetle affected forests in interior British Columbia (B.C.) in the 2000s. The impact of wolf predation on moose was not well-understood in the context of extensive landscape change. We monitored 33 wolves across 11 packs in 2 interior B.C. study areas: Prince George South (PGS), characterized by extensive salvage logging features, and John Prince Research Forest (JPRF), also affected by salvage logging but less intensively. Because predation risk is a function of wolf density, space use, and predation patterns, we required a better understanding of these factors to develop management recommendations that could minimize predation risk to moose. Based on mid-winter pack counts and home range size, wolf density was about 10 wolves/1,000 km2 in PGS and 5 wolves/1,000 km2 in JPRF. We identified 290 kills made by wolves, predominantly moose in PGS (87%) and JPRF (75%). Wolves in JPRF preyed on more elk and deer than did wolves in PGS, and at 10% of the kill sites we investigated in JPRF, wolves had killed black bears. We found moose calves at 27% of the moose kill sites, compared with mid-winter estimates of standing proportions of calves in the population of 13–20%. After accounting for probability of the collared wolf attending pack kills, we calculated that wolf packs in PGS killed a moose every 4-8 days in winter and every 8–11 days in summer. In JPRF, wolf packs killed a moose every 7–12 days in winter and every 19–26 days in summer. However, when we considered the number of wolves per pack in the 2 study areas, the kill rates per wolf were similar. Based on recent midwinter moose density esti- mates, these kill rates would equate to 7–20% of the moose population for PGS and 2–8% of the moose population for JPRF. These predation rates may not be indicative of predation rates during the moose decline in the 2000s, so it is important to consider the mechanisms that could contribute to changing kill rates, including differential use by wolves and moose of highly modified landscapes and landscapes exposed to recent change such as widespread logging or wildfire. Based on our 2 study areas, extensive salvage logging creates habitat features that may support higher wolf densities and larger pack sizes, particularly in landscapes where moose are the dominant ungulate species. ALCES VOL. 60: 19–43 (2024) Key Words: Alces alces, Canis lupus, kill rate, kill site, moose, predation rate, predation risk, probability of attendance, wolf Moose (Alces alces) are an important compo- nent of the ecological and sociocultural fabric of interior British Columbia (B.C., hereafter). They are a key species for consumptive and non-consumptive users, support rural and remote communities, and are critical to the *Present address: British Columbia Ministry of Water, Land and Resource Stewardship, 2080A Labieux Road, Nanaimo, B.C. V9T 6J9, Canada mailto:Morgan.Anderson@gov.bc.ca WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 20 cultural persistence of First Nations (B.C. FLNRO 2015, Gorley 2016). A mountain pine beetle (Dendroctonus ponderosae) outbreak in the late 1990s and early 2000s in central B.C. resulted in widespread lodgepole pine (Pinus contorta) mortality and subsequent salvage logging to reduce the spread and maximize utilization of dead standing pine (Alfaro et al. 2015). Moose populations declined over the same period throughout interior B.C. (Kuzyk 2016, Kuzyk et al. 2018). In 2013, the B.C. provincial government initiated a research project to determine the effects of landscape change on moose popu- lations (Kuzyk and Heard 2014, Kuzyk et al. 2019, Anderson et al. 2023). One of the knowledge gaps consistently identified by biologists, First Nations, and stakeholders was the role of wolf (Canis lupus) predation in moose declines in multi-prey, multi-predator systems (Kuzyk and Heard 2014, B.C. FLNRO 2014, Kuzyk et al. 2019). Wolves are an important cause of moose mortality (Gasaway et al. 1992, Bergerud and Elliot 1998, Hayes et al. 2003, Patterson et al. 2013, Mumma and Gillingham 2019), but several factors influence the vulnerability of moose to wolves. Many of these variables, including wolf density, pack structure, space use, and predation patterns are not well studied in interior B.C. Measuring wolf density or population trend is complicated and expensive, and relatively few wolf inventories have been done in B.C. (Kuzyk and Hatter 2014, Mowat et al. 2022). Wolves are not a species of conservation concern due to their high reproductive rates, adaptable life history, and high dispersal rates, thus inventories have not been prioritized. Instead, wolf density has been estimated provincially by an ungulate biomass index (Fuller et al. 2003, B.C. FLNRO 2014, Kuzyk and Hatter 2014). Our objective was to determine wolf density and predation patterns in 2 study areas overlapping the provincial moose research study areas. Although we were not able to assess wolf predation during the moose declines, we were able to examine wolf predation in 2 study areas of different disturbance regimes, a spatial proxy to investigate a mechanism that could have been consistent over time. STUDY AREA Both study areas were in north-central B.C. on the Interior Plateau, with rolling terrain approximately 800–1,500 m elevation, and characterized by a mosaic of coniferous and deciduous forests, lakes, and wetlands. The Prince George South (PGS) study area was located from the city of Prince George west to approximately the municipality of Vanderhoof, and from Highway 16 south to the Blackwater River. The John Prince Research Forest (JPRF) study area was cen- tered approximately over the research forest, from Fort St. James and the north shore of Stuart Lake north along the North Road to Inzana Lake in the east and Tchentlo Lake in the west. The dominant biogeoclimatic zones are Sub-Boreal Spruce, Sub-Boreal Pine–Spruce and Engelmann Spruce–Subalpine Fir at higher elevations (Meidinger and Pojar 1991). Dominant tree species varied by microsite, and included Douglas fir (Pseudotsuga menziesii), lodgepole pine (Pinus contorta), hybrid white spruce (Picea glauca x engelmannii), subalpine fir (Abies lasiocarpa), trembling aspen (Populus tremuloides), black cottonwood (Populus trichocarpa), and paper birch (Betula papyr- ifera). Summers were warm and dry (mean July temperature for study period was 16.3°C), and winters were cold (mean January temperature for study period was -6.5°C) with complete snow coverage (average maximum snow depth 55 cm) from November to March (ECCC 2024 for Fort St. James and Prince George airport weather stations). ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 21 Wildfires and forest harvesting were the dom- inant agents of landscape change, and the area underwent large-scale timber salvage har- vesting following a mountain pine beetle out- break in the early 2000s. We delineated study areas using the outer boundary of all wolf pack home ranges, 4,594 km2 in PGS and 4,119 km2 in JPRF (Figure 1). These study areas were chosen to approximate the moose study areas using the same names on a pro- vincial moose research project (Kuzyk and Heard 2014, Kuzyk et al. 2019). Moose were the dominant ungulate in both study areas. Rocky Mountain elk (Cervus canadensis) and mule deer (Odocoileus hemionus) are present in both study areas, although at higher densities in JPRF. White-tailed deer (Odocoileus virgin- ianus) were also present at low densities in both study areas. Caribou (Rangifer taran- dus) were absent from PGS but had been observed in JPRF, although infrequently. Domestic cattle (Bos taurus) were present on ranches and range tenures (public lands) Fig. 1. Prince George South (PGS) and John Prince Research Forest (JPRF) study areas (grey shading) for wolf predation research 2018-2022. Study areas for the partner project assessing mechanisms of moose decline are indicated as well (black outline). WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 22 during the summer. Besides wolves, large carnivores included black bears (Ursus americanus), grizzly bears (U. arctos) and cougars (Puma concolor). There were active wolf trappers in both PGS and JPRF and a liberal licensed hunting season, but effort varied and harvest monitoring was not reliable at a fine scale (Mowat et al. 2022). METHODS Between June 2018 and February 2021, we captured 34 wolves with soft-catch foot-hold traps (Livestock Protection EZ Grip #7) in summer and helicopter darting or net-gunning in winter. We chemically immobilized wolves with Zoletil™ (tiletamine-zolazepam) at 6–8 mg/kg. Once immobilized, we fitted wolves with radio-collars (VHF collars, Lotek Wireless, Newmarket, ON, Canada, or satellite GPS collars, Vectronic Aerospace, Berlin, Germany). Capture protocols were approved under B.C. Wildlife Act Permit PG17-272811, and were consistent with Sikes et al. (2016). We programmed GPS collars to collect one location every hour and to activate the mortality notification after 8 hours of immobility. Contact information on the collars allowed hunters and trappers to return any encountered collars. We used VHF collars to relocate packs in case the GPS-collared wolf died or dispersed, or the GPS collar malfunctioned. Wolf density We relocated collared wolves 1–5 times each winter to determine pack size from visual observation of wolves, and enumerating sets of tracks where wolf trails split apart on lakes, wetlands, and clearings. Although mid-winter pack counts were the standard for wolf abundance estimates (Boitani 2003), we also set up remote cameras at den and rendezvous sites to collect minimum counts of adults and pups over the summer, especially for packs that had a collar malfunction. We defined the study area as the outer boundary of 95% minimum convex polygons (MCPs) of each collared pack calculated in R 3.6.0 (R Core Team 2024) using the adehabitatHR package (Calenge 2006). MCP home ranges do not account for intensity of use but have been widely imple- mented and can be effective for delineating home range of territorial species (White and Garrott 1990, Mech and Boitani 2003). We determined home ranges annually for sum- mer (1 Apr – 31 Oct) and winter (1 Nov – 31 Mar), and considered the mean size of all seasonal home ranges over all years for a pack to be the overall home range size. For each pack, we determined the maximum winter pack count; for packs monitored for more than 1 year, we used the mean maxi- mum winter pack count as the pack size. We summed the pack size for all packs in the study area and applied it to the area delin- eated by the pack home ranges in the study area to estimate wolf density. In some years, packs dissolved and other packs expanded into their vacant territories, so applying densities calculated for each territory sepa- rately would have effectively double-counted parts of the study areas. Kill site investigations GPS cluster analysis has become a well-established technique for investigat- ing kill sites (Sand et al. 2005, Webb et al. 2008, Morehouse and Boyce 2011). We used the Find Points Cluster Identification Program v.2 (Gillingham 2009) to identify location clusters (and potentially kill sites, but see below) for each GPS-collared wolf, where a cluster was defined as a minimum of 2 locations within 100 m of each other within a 2-week (336 hours) period. We did not attempt to quantify predation on moose neonates (calves 4-6 weeks post-parturi- tion) or other small prey, which have short handling times and are unlikely to be ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 23 detected with hourly location fixes (Sand et al. 2005, Webb et al. 2008, Gable et al. 2016). We assumed that evidence of a kill would be present at any cluster that was actually a kill site during the site investiga- tion, which was generally weeks to months later. Kills made between November and March were rapidly buried by snow and were instead investigated after snow melt and prior to green-up in the spring. At each cluster, we searched the area for evidence of a kill (hair, rumen, and bones, often scattered over > 100 m) to determine prey species, sex (presence of antlers or antler pedicels), age (tooth wear and eruption, incisor extracted for cementum annuli aging animals > 1 year- old), and condition. To index body condi- tion, we collected and dried marrow from intact long bones, and quantitively assessed consistency, appearance, and fat content (white waxy marrow with high fat content or red gelatinous marrow with low fat content; Mech 2008). Marrow fat is the last fat store depleted, so presence of marrow fat did not necessarily indicate an animal in good con- dition, but lack of marrow fat was a defini- tive indicator of poor condition (Mech and DelGuidice 1985). We considered < 20% fat as acute malnutrition, 20-70% fat as poor body condition, and > 70% as good body condition (Procter et al. 2020). Marrow fat levels also vary with the specific bones selected for analysis (Spears et al. 2003), and although we collected a humerus or femur when available, we were often limited by which bones were left intact at kill sites. Marrow fat is also expected to be dependent on environmental conditions, with extended exposure leading to evaporation that leads to apparent higher fat content (Lamoureux et al. 2011, Murden et al. 2017). This further limited our sample size and constrained our inference because sample collection was weeks or months after death. Some moose identified at kill sites may have been scavenged by wolves but died from another cause. We removed obvious cases of scavenging from kill rate and preda- tion rate analysis (e.g. cut or sawed bones at hunter kills, radio-collared cow moose con- firmed killed by another proximate cause), but it remains possible that some carcasses we identified as having been wolf kills resulted from other causes. Characteristics of wolf prey at kill sites Determining selection requires both a measure of use, which we obtained from kill site investigations, and a measure of avail- ability. The latter was often not available for our variables of interest. We lacked density estimates for elk, deer, or black bears in either study area, as well as data on moose population age structure (Kuzyk et al. 2020). Similarly, we had no reference data regarding condition of moose in the population, which we expected to vary seasonally and annually. We considered using vehicle strikes to represent a random sample of moose body condition and age (because neither would be expected to predispose moose to vehicle strike mortality) but lacked a sufficient sample size. Data available to us included mid-winter stratified random block surveys that pro- vided bull and calf ratios for both PGS and JPRF. We flew these surveys in the Prince George West (PGW; equivalent to our PGS study area) and Fort St. James (FSJ; equiva- lent to our JPRF study area) survey areas in December 2016 and 2020 (Klaczek et al. 2017, Scheideman et al. 2021, Table 1). Midwinter calf ratios overestimate the num- ber of calves available to wolves throughout the entire winter because calf ratios decline over that period, and most wolf kills are in late winter and spring when calf ratios are lower (Procter et al. 2020). Calf numbers would be expected to drop by close to 30% WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 24 from mid-winter ratios up to recruitment at the mean birth date (May 21; Procter et al. 2020), suggesting that 13–22% of the popu- lation would be calves in December, but only 9–15% would be calves by late May. We considered the calf proportion in mid-winter and spring (midwinter adjusted to May) to examine whether wolves were selecting calves that were large enough to detect through cluster investigation. If wolves selected calves, we would expect to see a higher percentage of calves at kill sites than we see in the winter (or spring) moose popu- lation. Similarly, we would expect a higher proportion of bull moose at kill sites than in the population if wolves selected bull moose. Kill rates Following Vucetich et al. (2011), we defined kill rates as the number or biomass of prey killed by a predator (or biomass of a preda- tor) over a period of time. For wolves, this means accounting for the number (and/or size) of prey killed, the pack size, and the changing size and metabolic demands of pups as they grow (Mech and Peterson 2003). Seasonality is also important due to the presence of rapidly growing neonate prey (Mech and Peterson 2003, Sand et al. 2005, Metz et al. 2011). We calculated kill rates using moose that we concluded to have been killed by wolves, excluding scavenged hunter kills and biomass intake from other sources (e.g. other large or small prey species, bait sites set by hunters and trap- pers, dumped livestock carcasses). Predicting kill sites from cluster characteristics – In preliminary analyses, we used logistic regression models in R 3.6.0 (R Core Team 2024) to predict kill sites for summer, winter, both seasons, and study areas both separately and combined from cluster characteristics. All models con- sidered the probability of a cluster being a moose kill site as dependent variables, and the amount of time spent at a cluster (num- ber of location fixes and number of days between first and last visit to the site) and movements at the cluster (mean distance between cluster points and cluster centroid) as predictor variables. All models also incor- porated individual wolf identities as random factors, and we assessed the strength of evi- dence for each model using Akaike’s Information Criterion for small sample sizes (AICc). The low power all models had to predict kill sites led us to ultimately abandon this line of inquiry (see Results). Assessing complete kill time series – We investigated all likely kill sites to deter- mine the number of moose killed over a defined period (Fuller and Keith 1980, Fuller 1989, Palm 2001). We ground-truthed all clusters with more than 15 locations in the Table 1. Mid-winter calf and bull moose proportions for the Prince George South (PGS) and John Prince Research Forest (JPRF) study areas based on moose surveys conducted in Prince George West (PGW) and Fort St. James (FSJ) in north-central B.C. (Klaczek et al. 2017, Scheideman et al. 2021). Adjusted moose calf proportions account for 30% overwinter mortality expected between mid-winter surveys and recruitment to age 1 in late May. PGS (PGW) JPRF (FSJ) Calf proportion Bull proportion Calf proportion Bull proportion December 2016 0.20 0.21 0.22 0.20 Adjusted to May 2017 0.14 0.15 December 2020 0.21 0.24 0.13 0.28 Adjusted to May 2021 0.15 0.09 ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 25 2-week temporal window. For wolves with less predictable movement patterns at kill sites, we also visited additional clusters with fewer than 15 locations in the 2-week tempo- ral window, especially when there was a long duration between confirmed kills. We did not include time series less than 2 weeks in dura- tion and considered only one wolf if 2 wolves in the pack were comprehensively monitored with active GPS collars at the same time. For time series that overlapped our defined winter (1 Nov – 31 Mar) to summer (1 Apr – 31 Oct) seasons we assigned the season that was pre- dominantly represented by the environmental conditions present. Probability of attendance – The number of kills attended by an individual collared wolf underestimates the number of kills made by the pack because the collared wolf may not be present at all kills. To account for this, we corrected for the probability of the collared wolf not being at a kill that the other members of the pack have made. Probability of atten- dance varied by season, wolf age, pack size, and prey size in Yellowstone National Park (YNP, Metz et al. 2011). We considered the correction factors calculated for YNP (proba- bility of attendance in summer = 0.68, proba- bility of attendance in winter = 0.95, Metz et al. 2011) and created correction factors spe- cific to our study areas using data from 3 packs in PGS with 2 wolves GPS-collared simultaneously. We used Metz et al.’s (2011) double-observer approach to estimate proba- bility of attendance at kill sites by treating each wolf as an observer and their presence at the kill as a detection. The total number of detections was given by: ( )( ) = + + + − 1 1 1 1N N N Ntotal A B AB (1) where Ntotal was the total number of detections, NA and NB by pack member A or pack member B, and NAB referred to detec- tions by both pack members A and B. The probability of detection (PD) for each pack member A and B was: = =andPD N N PD N NA AB B B AB A (2) Predation rates The predation rate is the proportion of a prey population killed by a predator over a speci- fied period. We flew stratified random block surveys to estimate moose abundance in December 2020 in the PGW and FSJ survey areas (representing PGS and JPRF study areas respectively). We estimated moose densities in PGW as 0.62 (SE = 0.05) moose/ km2 and 0.84 (SE 0.12) moose/km2 in FSJ (Scheideman et al. 2021, sightability correc- tion following Quayle et al. 2001). These densities extrapolated to 2,849 moose (95% CI = 2,390–3,308) in the PGS study area, and 3,460 moose (95% CI = 2,531–4,388) in the JPRF study area (in both cases including 8-month-old calves). We used these popula- tion estimates and the corrected kill rates to estimate the predation rate by wolves on the moose population in each study area. RESULTS Wolf density We monitored 33 wolves in 11 packs (6 in PGS, 5 in JPRF) representing contiguous wolf territories in the study areas, except for one unmonitored pack territory in PGS where wolves were seen but collars never deployed (Tables 1, 2). That area was not included in density calculations. The mean collar deployment period accounting for mortality, dispersal out of the study area, and malfunction was 277 days. Home range size of wolf packs varied from 250 to 1,100 km2 (Table 3). Mean home range sizes of packs in the 2 study areas did not differ in summer WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 26 Table 2. Summary of wolf packs monitored in 2 interior British Columbia study areas for a study of wolf predation on moose, 2018-2022. Study Area Pack GPS-collared wolves VHF-collared wolves Total wolf-days monitored Monitoring Start Monitoring End PGS Blackwater River 1 male 1 female 356 06 Mar 2018 25 Feb 2019 Bobtail Mountain 2 male, 1 female 1 female 608 22 Feb 2018 11 Mar 2022 Clear Lake 2 male, 1 female 1 male, 1 female 470 06 Mar 2018 13 Feb 2020 Ghost Pack 1 male 1 male 88 27 Feb 2020 25 May 2020 Grizzly Lake 2 male, 2 female 1 female 338 28 Feb 2020 19 Dec 2021 Tagai Lake 2 male 4 female 1070 05 Jan 2019 22 Mar 2021 Tatelkuz Lake 1 malea none 97 20 Jan 2019 27 Apr 2019 JPRF Hat Lake 1 male, 2 female none 751 09 Jun 2018 23 Nov 2020 Kazchek Lake 2 male 1 female 822 12 Jul 2019 07 Feb 2022 Pinchi Lake 1 female none 282 16 Jun 2018 25 Mar 2019 Tachie 1 male none 255 06 Feb 2019 19 Oct 2019 Tanizul Lake 1 male none 381 08 Feb 2019 24 Feb 2020 aThis individual was collared in the Bobtail Mountain pack then dispersed to Tatelkuz Lake. Table 3. Home range sizes based on 95% minimum convex polygon (MCP) and mean maximum midwinter pack counts for wolf packs monitored in Prince George South (PGS) and John Prince Research Forest (JPRF) in north-central B.C., 2018-2022, with density calculated at the home range scale and over the study area. Mean Pack Count Range (min-max) Pack Count Summer Range (km2) Winter Range (km2) Overall Range (km2) Density (wolves/ 1,000 km2) PGS Blackwater 7.0 7 469 400 423 16.6 Bobtail Mountain 10.5 7-14 1247 445 712 14.7 Clear Lake 9.0 7-12 408 897 702 12.8 Ghost Pack 5.0 5 315 195 255 19.6 Grizzly Lake 7.5 6-9 424 513 454 16.5 Tagai Lake 5.5 5-6 880 556 695 7.9 Study area (6 packs) 44.5 37-53 4595 9.7 JPRF Hat Lake 5.0 4-7 1006 512 723 6.2 Kazchek Lake 3.7 3-5 1292 827 1059 3.5 Pinchi Lake 4.7 3-8 183 410 296 15.9 Tachie 3.0 2-4 832 574 660 4.5 Tanizul Lake 4.5 3-6 441 340 390 11.5 Study area (5 packs) 20.9 15-30 4119 5.1 ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 27 (2-tailed test t = ̠ 0.15, P = 0.88, df = 7), win- ter (2-tailed test t =0.97, P = 0.35, df = 9), or year-round (2-tailed-test, t = 1.41, P = 0.19, df = 9). We made 69 observations of pack size to determine pack counts for 28 pack- years, including 6 estimates for packs with- out functioning GPS collars (by locating VHF-collared wolves or den monitoring). Wolf packs were larger in PGS (7.4 wolves/ pack) than in JPRF (4.2 wolves/pack) (2-tailed-test, t = 3.26, P = 0.01, df = 9). We estimated a density of 9.7 wolves/1,000 km2 in PGS and 5.1 wolves/1,000 km2 in JPRF, with much higher densities within some pack home ranges (Table 3). Density of monitored packs was estimated at 1.9 packs/1,000 km2 in PGS and 1.6 packs/1,000 km2 in JPRF. Characteristics of wolf prey at kill sites We investigated 1,208 clusters between March 2018 and July 2022 (908 in PGS and 300 in JPRF). The sample size difference reflected differences in collar deployment times, logistics, and how often the collared wolves made kills. In PGS, we investigated clusters a mean of 111 days after cluster formation (SD = 83 days, n = 892; investi- gation date was not recorded for some clus- ters) and in JPRF a mean of 97 days after cluster formation (SD = 101 days, n = 293; investigation date was not recorded for some clusters). We initially visited clusters regardless of the number of points in the cluster, but we consistently found no evidence of a carcass at small clusters (< 10 location fixes) and instead prioritized larger clusters with > 15 location points. Mean number of points in visited clusters was 28.1 (95% CI = 26.0–30.2); mean number of points in all clusters was 8.3 (95% CI = 8.1–8.5). Clusters were associated with dens, rendezvous sites, bed sites, old kill sites, illegal garbage dump sites, bait piles set by hunters and trappers, gut piles left by hunters, beaver (Castor canadensis) activ- ity, snowshoe hare (Lepus americanus) activity, shed antlers that had been chewed, and kill sites. Of the 290 kill sites identi- fied, most were moose in both PGS (n = 200) and JPRF (n = 52). Other species included elk, deer, domestic cattle (PGS only), and black bear (Table 4). The pack targeting cattle was subsequently removed by the B.C. Cattleman’s Association Livestock Protection Program and a new pack moved into the vacant territory within months. Moose kill characteristics – Of the 252 moose kill sites detected, we determined sex for 108, including 7 female calves and 6 male calves. Of 95 adult moose with sex confirmed, we found 76 cows and 19 bulls. Comparisons of the 20% bulls in the killed sample with the observed bull ratios from moose surveys (16.4% in 2016-17, n = 729, Klaczek et al. 2017; 20.8% in 2020-21, n = 1399, Scheideman et al. 2022) provided no evidence that wolves selected moose based on sex in our study areas (χ2 = 0.778, df = 1, P = 0.38 for 2016-17; χ2 = 0.035, df = 1, P = 0.85 for 2020-21). Of the 252 moose kill sites detected, we found 69 calves, 175 adults (including 22 yearlings), and 8 moose of unknown age class. Based on our kill site investigations, 28% of wolf-killed moose were calves (95% CI = 23–34%), higher than the proportion of Table 4. Proportion of species detected at wolf kill- sites in Prince George South (PGS, n = 225) and John Prince Research Forest (JPRF, n = 65) in north-central B.C., 2018-2022. One elk kill was detected in PGS (0% due to rounding). Prey species Proportion of kills, PGS Proportion of kills, JPRF Moose 0.87 0.75 Black bear 0.02 0.10 Deer 0.05 0.06 Elk 0.00 0.06 Domestic cattle 0.04 NA Unknown 0.02 0.02 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 28 calves in the population (13–22% of the mid-winter population; 9–15% of the population by May). We aged 71 wolf-killed moose > 1 year of age using cementum annuli from incisors (Figure 2). The mean age of all wolf-killed moose (not including calves) in our study was 7 years (95% CI = 6 – 8, n = 98, range 1–17). The mean age of wolf-killed bull moose (5 years, 95% CI = 3 – 7, n = 15) was lower than the mean age of wolf-killed cow moose (9 years, 95% CI = 3 – 7, n = 53) (2-tailed-test, t = 2.84, P = 0.006, df = 66). However, when we considered only the 9 bulls and 48 cows > 1 year of age (bulls – 8 years, 95% CI = 6 – 10, cows – 10 years, 95% CI = 9 – 11), there was no difference in age between wolf-killed bull and cow moose (2-tailed-test, t = 1.34, P = 0.19, df = 55). We analysed marrow fat content from 31 long bones from adult moose and 4 from calves. Calf samples had a mean of 91% marrow fat (95% CI = 84 – 98%), however, the sample size was very small (n = 4). Adult marrow samples had a mean of 87% marrow fat (95% CI = 82 – 92%). We considered 1 moose to be acutely malnourished (8.5% marrow fat) and 2 in poor body condition (58% and 68% marrow fat). Based on 18 qualitative assessments of marrow, we noted 14 as whitish and solid (77%), 2 as pinkish red and solid (11%), and 2 as red and dried (11%). Although sample sizes were small and marrow fat percentage likely biased high even for samples collected and frozen relatively quickly, marrow fat content of wolf-killed moose was high overall. Kill rates Predicting kill sites from cluster charac- teristics – We considered several candidate models to predict the probability of a clus- ter representing a kill, but most models pro- vided unrealistic results, especially for smaller cluster sizes. Most models sug- gested about a 10% probability of a cluster representing a kill if there were 2 hourly locations in the cluster, which would have generated a drastic overestimation in the number of kill sites. Wolf behaviour at kills also varied by individual, and with a mean collar deployment of 277 days, we were unlikely to have sufficient kills for individ- ual-specific predictive models. Number of points in the cluster was consistently a significant variable in seasonal study area Fig. 2. Cementum annuli ages from incisor teeth collected from 71 moose 2 years or older killed by wolves in north-central B.C., 2018-2022. Calves and yearlings are not included as sample sizes for those age classes were much higher (incisors did not need to be recovered to estimate the age based on tooth eruption). ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 29 models, many of which had equivalent AICc scores for both large prey kills (Appendix 1) and moose kills specifically (Appendix 2). Clusters with more location points were more likely to represent kills, but this relationship was not as clear in summer, especially in JPRF (Figure 3); clusters in summer were often associated with rendezvous sites and dens, which obscured the relationship between per- sistence time at a site and likelihood of kill. We also noted considerable variability in the coefficient estimates and prediction of whether a cluster represented a kill site, even for large kill sites, compromising our ability to predict whether a cluster site rep- resented a kill, or a moose kill specifically. Thus, we declined to extrapolate kill sites from cluster sites to subsequently deter- mine kill rates or predation rates. Assessing complete kill time series – We tracked 50 complete kill chronologies for PGS and 22 for JPRF, averaging about 5 weeks long and representing all collared packs. This provided a kill rate of 0.64 moose/week/pack or a moose every 12 days in PGS and 0.34 moose/week/pack or one moose every 21 days in JPRF. Seasonal kill rates for PGS were one moose every 15 days in the summer and every 8–9 days in the winter and for a pack in JPRF, one moose every 37–38 days in the summer and every 13 days in the winter. Probability of attendance – Three packs in PGS had multiple GPS-collared wolves simultaneously: Tagai Lake pack (breeding male and a subordinate male, 25 March 2019–13 December 2019), Clear Lake pack (2 subordinate males, 21 January 2019–7 March 2019), Grizzly Lake pack (yearling female, subordinate male, and sub- ordinate female, 23 June 2019–1 July 2021). The Tagai Lake pack’s breeding wolf was present at 17 moose kills and a black bear kill, and its subordinate wolf was present at 13 moose kills. We detected 22 kills made by this pack over the 257-day period when both collars were active (PD = 0.69 for breeder, PD = 0.50 for subor- dinate), resulting in an estimated 26 kills (0.7 kills/week). Considering the summer Fig. 3. Probability that location clusters represent a wolf kill site of a large prey in summer (a) and winter (b), and probability of a winter cluster representing a moose kill specifically (c), based on number of location points in the cluster for Prince George South (PGS) and John Prince Research Forest (JPRF), north- central B.C. 2018-2022. Shaded area is 95%CI. a b c WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 30 season only (183 days and 16 individual kills detected, 13 by the breeder and 10 by the subordinate), PD was 0.70 for the breeder and 0.54 for the subordinate. Only 6 kills were recorded for 46 days of winter monitor- ing for Tagai Lake, 5 by the breeder (PD = 0.67) and 3 by the subordinate (PD = 0.40). For Clear Lake pack, one wolf was pres- ent at 7 kills and the other at 9 kills. We doc- umented 13 individual moose kills during the 56-day period when both collars were active (PD = 0.33 and 0.43), resulting in an estimated 21 moose killed (2.6 moose/week). We documented only a single moose kill in the short period of time in which the Grizzly Lake pack had 3 functioning collars. We considered both the YNP seasonal probability of attendance (Metz et al. 2011) and the probability of attendance based on breeding status for the PGS wolves, assign- ing a probability of attendance of 0.4 to subordinate wolves and 0.7 to breeders as an approximation, realizing the sample size is low. These correction factors substan- tially increased the estimated kill rates for packs in both PGS and JPRF (Table 5). Kill rates per wolf were similar between PGS and JPRF, despite the differences in pack size (Table 6). Predation rates After adjusting for probability of attendance by the collared wolf at a kill, the predation rates on moose were 13.7% (11.8–16.4%) in PGS and 5.1% (4.0–6.9%) in JPRF year- round with higher estimates in the winter than summer (Table 7). DISCUSSION Wolf density Across their North American range, wolves generally occur at densities from 2 to 40 wolves per 1,000 km2 (Paquet and Carbyn 2003 and references therein). The wolf densities we calculated for PGS (10 wolves/1,000 km2) and JPRF (5 wolves/1,000 km2) are similar to estimates calculated from the ungulate biomass index (6.9–13.7 wolves/1,000 km2, Kuzyk and Hatter 2014). Our density estimate would have been slightly higher had we also accounted for lone wolves, which typically make up 10–15% of the population (MN DNR 2001, Baer 2011). The wolf density in PGS and JPRF was lower than the density estimated from snow track surveys in February and March 2017 when tracking conditions were suboptimal with low snowpack and melted areas that made it difficult to follow wolf Table 5. Kill rates adjusted for probability of attendance based on means for winter and summer calculated in Yellowstone National Park (YNP) of 0.68 in summer and 0.95 in winter (Metz et al. 2011) and based on Clear Lake and Tagai Lake packs in 2019 for subordinate wolves (0.4) and breeders (0.7). Unadjusted kill rates were based on periods of continuous monitoring 2018-2022 (JPRF n = 10 winter, 12 summer; PGS n = 17 winter, 22 summer). Unadjusted kill rate PGS-adjusted kill rate YNP-adjusted kill rate Moose/pack/ week (± SE) Days/ moose/pack Moose/ pack/week Days/ moose/pack Moose/ pack/week Days/moose/ pack PGS Annual 0.64 ± 0.06 11.0 1.25 5.6 Winter 0.87 ± 0.09 8.0 1.75 4.0 0.92 7.6 Summer 0.45 ± 0.05 15.5 0.87 8.0 0.66 10.5 JPRF Annual 0.34 ± 0.07 20.6 0.67 10.4 Winter 0.55 ± 0.12 12.7 1.08 6.5 0.58 12.1 Summer 0.19 ± 0.06 37.5 0.38 18.6 0.28 25.5 ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 31 trails, making continuous trails appear to be several separate detections (Anderson et al. 2017). This emphasized the importance of good conditions for wolf snow track sur- veys. Wolf density is also expected to fluctu- ate widely between seasons, with large litters of pups observed in both study areas during this project. Changing survival and dispersal rates for pups and adults would also change density estimates among years. Home range size (250–1,100 km2) was within the reported values for wolf populations elsewhere in northern North America (Mech and Boitani 2003) and similar between study areas. The difference in wolf density between the 2 study areas was evidently driven by pack size rather than by the number of packs. Pack size may be as important to preda- tion rates as wolf density in some systems because smaller packs lose more biomass from a kill to scavengers and can therefore have a higher per wolf kill rate (Vucetich et al. 2004, Kaczensky et al. 2005). The dif- ference in pack size had also been noted in the 2017 track surveys despite suboptimal survey conditions (5-7 wolves per pack in PGS, 3-5 wolves per pack in JPRF, Anderson et al. 2017). Below, we consider several hypotheses to explain differences in pack size between the 2 study areas. Table 6. Kill rates per wolf adjusted for probability of attendance based on means for winter and summer calculated in Yellowstone National Park (YNP) of 0.68 in summer and 0.95 in winter and based on Clear Lake and Tagai Lake packs in 2019 for subordinate wolves (0.4) and breeders (0.7). Unadjusted kill rates were based on periods of continuous monitoring 2018-2022. Mean midwinter pack sizes were 4.2 wolves in John Prince Research Forest (JPRF) and 7.4 wolves in Prince George South (PGS), north-central B.C. Biomass assumes 297 kg/moose based on kills in the Yukon (Kaczensky et al. 2005, Hayes et al. 2000). Unadjusted kill rate (moose/wolf/ week) ± SE PGS-adjusted kill rate YNP-adjusted kill rate (moose/wolf/week) Moose/wolf/week Kg/wolf/day Moose/wolf/week Kg/wolf/day PGS Winter 0.12 ± 0.01 0.24 10.2 0.12 5.1 Summer 0.06 ± 0.01 0.12 5.1 0.09 3.8 JPRF Winter 0.13 ± 0.03 0.26 11.0 0.14 5.9 Summer 0.04 ± 0.01 0.09 3.8 0.07 3.0 Table 7. Estimated predation rates on moose (proportion of moose killed) 2018-2022 in Prince George South (PGS; 6 packs, mean pack size 7.4 wolves) and John Prince Research Forest (JPRF; 5 packs, mean pack size 4.2 wolves) using moose densities from winter 2020-21, north-central B.C. (Scheideman et al. 2021). The 95% CI refers to the moose population estimate; mean kill rates were applied after adjusting for probability of attendance based on PGS wolves and by values reported from wolves in Yellowstone National Park (YNP, Metz et al. 2011). Study Area Season PGS-adjusted predation rate YNP-adjusted predation rate Mean 95% CI Mean 95% CI PGS All 13.7% 11.8-16.4% Winter 19.2% 16.5-22.9% 10.1% 8.7-12.0% Summer 9.5% 8.2-11.4% 7.3% 6.3-8.7% JPRF All 5.1% 4.0-6.9% Winter 8.1% 6.4-11.1% 4.4% 3.4-6.0% Summer 2.8% 2.2-3.9% 2.1% 1.6-2.8% WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 32 Wolf pack size is related to prey avail- ability and competition with other wolves. Packs tend to be larger where the primary prey is also large, because the biomass obtained from a large kill can support more wolves, and higher prey densities support larger packs (Sells et al. 2022). Large packs are also more successful than small packs at killing prey (MacNulty et al. 2012). In both study areas, moose are the primary prey and moose densities are similar, suggesting that prey availability may not be the primary driver of pack size differences. Wolf pack sizes are generally stable under low harvest but can decline under high harvest (Sells et al. 2022), and pack occupancy is generally stable even with high harvest and high turn- over of individuals (Bassing et al. 2019). Without reliable harvest data (Mowat et al. 2022), we were unable to assess this as a driver for pack size between the 2 sites. When the breeding female or breeding pair is removed from a pack, the pack often dis- solves (Brainerd et al. 2008, Borg et al. 2015), which can potentially increase local wolf density and number of packs (Ballard and Stephenson 1982, Mech and Boitani 2003). Harvest data was not sufficiently reli- able to fully assess the impacts of harvest on wolf pack structure during our study. As wolf density increases, competition for lim- ited resources also increases and larger packs are better at defending territories than smaller packs (Smith et al. 2010, Sells et al. 2022). Densities at which intraspecific aggression is expected to regulate wolf pop- ulations are much higher than either of our study areas (69 wolves/1,000 km2, Cariappa et al. 2011), suggesting that the role of intra- specific competition in driving wolf pack size and population dynamics is likely over- shadowed by environmental factors and food availability driving recruitment and dispersal (Fuller 1989, Bergerud and Elliot 1998, Hayes and Harestad 2000). Characteristics of wolf prey at kill sites Moose were the dominant prey in both PGS and JPRF as estimated using cluster searches, although wolves in JPRF consumed more elk, deer, and black bears than in PGS. Although formal inventories have been spa- tially limited for elk (Scheideman and Anderson 2022, 2023; Scheideman et al. 2024) and deer (B.C. Ministry of Water, Land and Resource Stewardship unpub- lished data), we suspect densities of these other ungulates were higher in JPRF than PGS based on discussions with local stake- holders and First Nations. Black bear density estimates were not available in either study area. We did not assess the use of small prey, but cluster sites with few (< 10) points were frequently associated with beaver dams, lodges, and foraging activity, especially in JPRF. Small prey can make up a significant proportion of wolf diets, especially in sum- mer (Metz et al. 2012, Gable et al. 2016, Gable et al. 2018), but a more frequent fix rate and more intensive ground searches than we undertook would be needed to quan- tify the use of small prey in our study areas. Wolves in both PGS and JPRF preyed on moose calves in higher proportions than they were available in the population. This, together when considering that calf kills were more likely to be underrepresented or classified as unknown due to their smaller size and more complete consumption, sug- gests that wolves in our study areas selected calves. Without baseline population data, we were not able to definitively show selection by wolves for moose of other age classes. Intuitively, the consistent presence of older moose at kill sites would suggest selection for older age classes (Mech et al. 1995), because they would naturally decline in abundance in the moose population. Kuzyk et al. (2020) analyzed the age of more than 2,000 hunter-killed moose from 1982–2003, 15 years prior to the beginning of our study ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 33 and over a period of increasing and high moose density. They found the mean age of harvested cow moose to be 4 years old, much younger than the mean age of 10 years old for collared cow moose dying from all causes 2012–2020 in interior B.C. (n = 47, Procter et al. 2020). The difference between mean age of death identified in the 2 studies is likely due to inclusion in the latter of nat- ural mortality that differentially affects older individuals, but the age structure may also have changed following widespread popula- tion declines. The mean age of wolf-killed, non-collared adult cow moose (> 1 yr old) from our study was similar to Procter et al.’s (2020) reported mean age of wolf-killed, collared cow moose (> 1 yr old) in the same study areas (10 yrs, 95% CI = 8.6 – 11.6, range 1–16 yrs). Additionally, we did not detect selection of wolves for either sex of moose in our study areas, although other studies have found selection for yearling or adult bulls (Fuller and Keith 1980). We lacked data to compare the body condition of wolf-killed moose with the live moose or moose dying of other causes. We were limited to inferring body condition from marrow fat, which is only one parame- ter to quantify moose health. Relatively high marrow fat content of the 4 calves could be expected, as calves increase marrow fat con- tent from birth through the fall (Spears et al. 2003) and we were sampling older calves, not neonates. Carstensen et al. (2017) found that at least 40% of the moose killed by wolves in their study had underlying health issues that may have predisposed them to predation, and poor body condition has been identified as an important mortality factor for moose in this study area and elsewhere (Mumma and Gillingham 2019, Cook et al. 2021, Anderson et al. 2023). We were unable to make any conclusions about other aspects of health that could have predisposed an ani- mal to predation. Kill rates False negatives, where a kill was present but not detected by observers, may have occurred if we were unable to locate any remains due to carcasses being fully con- sumed or moved by wolves or by scaven- gers. Blecha and Aldredge (2015) used a double-observer approach to estimate a 4% false negative rate on cougar kills investi- gated 4–60 days after the kill in Colorado, suggesting a relatively low rate of false negatives if our study system is similar. Moose leave larger remains that are easier to detect compared to smaller species like deer which may reduce the prevalence of false negatives in our study areas. Estimating kill rates of social carnivores requires consideration of how often mem- bers of the social group are foraging together. Under conditions when packs are not as cohesive, including for larger packs (Jedrzejewski et al. 2001), we would expect lower probabilities of attendance at kill sites. The probabilities of attendance calculated for PGS wolves were lower than those of YNP wolves, but packs in our study were smaller than the mean 13 wolves/pack in YNP. Thus, pack size would not appear to explain lower attendance in PGS. Other fac- tors in our study areas may lead to lower pack cohesion, including active hunting and trapping of wolves which can cause pack splitting. Also, the high densities of roads and cutblocks may facilitate packs splitting up to increase search efficiency. We also did not find the same clear seasonal difference in pack cohesion as Metz et al. (2011). We would expect packs to be more cohesive in winter with individuals travelling together and therefore more likely to be at the same kills (Peterson et al. 1984); we did not detect a difference, but this may be more likely a sample size issue than a behavioural differ- ence. The energetic efficiency of travelling as a pack in deep snow, the summer WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 34 availability of small prey that can be effec- tively hunted by individual wolves, and behaviours associated with denning are all expected to influence pack cohesiveness and may not differ substantially between our study areas and the system examined by Metz et al. (2011). Kill rates of moose were higher in PGS (4-11 days between moose kills) than in JPRF (8-26 days between moose kills). However, the difference in pack sizes meant that kill rate per wolf was similar between PGS (mean pack size 7.4 wolves) and JPRF (mean pack size 4.2 wolves). Differences in moose kill rates between the 2 study areas were largely due to differences in pack size. This contrasted with results from work in other wolf populations with similar pack sizes that indicated higher kill rates per wolf by smaller packs (Ballard et al. 1987, Hayes et al. 2000). This difference may have resulted from different prey availability or minimal loss to scavengers even for small packs in PGS and JPRF. Predation rates Fuller and Keith (1980) estimated predation rates in a wolf-moose-caribou system in northeastern Alberta as 11-12% of the adult moose population, but high calf production suggested that the moose population could sustain that predation rate. Hayes et al. (2000) estimated predation rates in the Yukon of 10-15% of all moose and 7-16% of adult moose in winter, similar to the preda- tion rates we reported here for PGS and higher than for JPRF. Annual predation rates in both study areas appear to be within the ranges reported for stable to increasing moose populations, which is consistent with recent survey results indicating stable to increasing moose populations in both PGS and JPRF (Scheideman et al. 2021). Other interacting factors are important to consider beyond wolf predation rates. In Alaska, moose populations declined follow- ing severe winters when exposed to predation rates of 13-34% of the winter population in addition to hunting pressure (Gasaway et al. 1983, Gasaway et al. 1992). Predation rates of 10-15% in the Yukon were observed for moose populations increasing from low to moderate density, but would be expected to vary with prey density, alternate prey avail- ability, and prey vulnerability (Hayes et al. 2000). Wolves exploiting relatively naïve and high-density moose populations in Sweden killed 4-15% of the winter moose population, mostly calves (Palm 2001). The age classes targeted by wolves and the extent to which wolf predation is additive versus compensa- tory on moose populations will also influence the sustainability of any given predation rate. The role of wolf predation in multi-prey systems has been extensively examined to determine whether or under what conditions it may be a limiting or regulating factor (Mech and Peterson 2003). Limiting factors act on populations in a density-independent manner, while regulating factors act in a density-dependent manner, i.e., they differ- entially affect the population based on its density. The functional response of wolves and the density and carrying capacity of moose are important considerations for the outcome of wolf predation in a given sys- tem. Messier (1994) found that wolf preda- tion is density dependent when moose densities are < 0.65 moose/km2 but inversely density dependent at higher moose densities. A predator pit occurs when predation on a low-density population is high enough to prevent it from expanding to a higher den- sity equilibrium point, and can occur in sys- tems with high carrying capacity and high predation stochasticity (Clark et al. 2021). Predator pits have been documented in moose-wolf systems in Alaska (Gasaway et al. 1983, Boertje et al. 1996, Regelin et al. 2005). However, if habitat quality is poor, ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 35 low density stable states are more likely the cause of low-density populations than a predator pit (Clark et al. 2021); removal of wolf predation may result in increased prey densities, but when wolf predation is reintro- duced prey densities do not remain at a high equilibrium point (Gasaway et al. 1992, Mech and Peterson 2003). Continued moni- toring of interior B.C. moose population dynamics as the extensive pine beetle sal- vage harvest cutblocks reach successional stages selected by moose (Mumma et al. 2021) will be essential to determining whether a predator pit is a possibility. Alternatively, habitat quality may be a more important driver than predation alone in changes to moose density in our study areas, especially considering how habitat change interacts with predation. The interaction with other prey species, especially small prey that may make up a significant contri- bution to biomass intake (Gable et al. 2018, Anderson et al. 2025), is also unknown. The way in which wolves use salvage logging features also has implications for predation rates. Boucher et al. (2022) exam- ined wolf movement and habitat selection in PGS to determine habitat features used and selected by wolves and where wolves were killing moose. Moose kill sites had a higher probability of occurring in areas with higher proportions of new cutblocks (0–8 yrs post-harvest, selected by wolves, but gener- ally not by moose; Scheideman 2018, Mumma et al. 2021) and regenerating cut- blocks (9–24 yrs post-harvest, not generally selected by wolves but selected by moose; Scheideman 2018, Mumma et al. 2021). This appears to represent an intersection of differing selection patterns that allows over- lap between predator and prey. Moose kill sites were also less likely to occur in decidu- ous stands, which were generally not selected by wolves but consistently selected by moose (Scheideman 2018). Although moose habitat selection may be a trade-off between minimizing predation risk and maximizing forage intake (Francis et al. 2021), maintain- ing deciduous stands on the landscape may be important to moose for forage and as areas of lower predation risk. Spatial varia- tion in wolf predation risk resulted in higher predation risk for seasonally migratory moose than for moose that remained on the same home range year-round, which can have implications for survival and recruit- ment (Koetke 2024). The results of this and related research underscore the importance of considering how predation dynamics could change based on widespread land- scape disturbance, rather than predator or prey abundance alone. ACKNOWLEDGEMENTS Funding for the project was provided by the Habitat Conservation Trust Foundation (Project 7-473), Forest Enhancement Society of B.C., and the Province of British Columbia Land-based Investment Strategy. Additional in-kind and logistical assistance was pro- vided by the John Prince Research Forest (University of Northern B.C., Tl’azt’en Nation, and Binche Whut’en). Thanks to everyone who assisted with fieldwork and provided input throughout the project. Pauline Priadka, William Severud, Rich Harris, and an anonymous reviewer pro- vided helpful comments on previous ver- sions of the manuscript. LITERATURE CITED AlfAro, R.I., L. vAn Akker, and B. HAwkes. 2015. Characteristics of forest legacies following two mountain pine beetle out- breaks in British Columbia, Canada. Canadian Journal of Forest Research 45:1387–1396. Anderson, M., H. D. Cluff, L. D. MeCH, and D. R. MACnulty. 2025. Wolf density and predation patterns in the Canadian WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 36 High Arctic. Journal of Wildlife Management 89: e22671. Anderson, M., M. klACzek, S. MArsHAll, and A. BAtHo. 2017. Minimum count snow track survey of wolves in Prince George South (WMU 7-10, 11, 12) and John Prince Research Forest (WMU 7-25, 26, 28), winter 2017. B.C. Ministry of Forests, Lands and Natural Resource Operations, Prince George. Anderson, M., C. ProCter, M. sCHeideMAn, D. Hodder, H. sCHindler, and H. BoHM. 2023. Factors affecting moose popula- tion declines in British Columbia: Summary and Recommendations 2012- 2022. B.C. Ministry of Forests, Victoria. BAer, A. 2011. Wolf survey in the Nisutlin River basin, 2011. Yukon Fish and Wildlife Branch Report TR-11-17. BAllArd, W. B., and R. O. stePHenson. 1982. Wolf control – take some and leave some. Alces 18:276–300. BAllArd, W. B., J. S. wHitMAn, and C. L. GArdner. 1987. Ecology of an exploited wolf population in south-central Alaska. Wildlife Monographs 98:3-54. BAssinG, S. B., D. E. AusBAnd, M. S. MitCHell, P. lukACs, A. keever, G. HAle, and L. wAits. 2019. Stable pack abundance and distribution in a har- vested wolf population. Journal of Wildlife Management 83:577–590. BerGerud, A.T. and J.P. elliot. 1986. Dynamics of caribou and wolves in northern British Columbia. Canadian Journal of Zoology 64:1515-1529. BleCHA, K. A., and M. W. AlldredGe. 2015. Improvements on GPS location cluster analysis for the prediction of large carni- vore feeding activities: ground-truth detection probability and inclusion of activity sensor measures. PLoS ONE 10:e0138915. Boertje, R. D., P. vAlkenBurG, and M. E. MCnAy. 1996. Increases in moose, cari- bou, and wolves following wolf control in Alaska. The Journal of Wildlife Management 60:474–489. BoitAni, L. 2003. Wolf conservation and recovery. Pages 317-340 in L. D. Mech and L. Boitani (ed.) Wolves: behavior, Ecology and Conservation. University of Chicago Press, Chicago, IL. BorG, B. L., S. M. BrAinerd, T. J. Meier, and L. R. PruGH. 2015. Impacts of breeder loss on social structure, repro- duction and population growth in a social canid. Journal of Animal Ecology 84:177–187. BouCHer, N. P., M. Anderson, A. lAdle, C. ProCter, S. MArsHAll, G. kuzyk, B. M. stArzoMski, and J. T. fisHer. 2022. Cumulative effects of widespread land- scape change alter predator-prey dynam- ics. Scientific Reports 12:11692. BrAinerd, S. M., H. Andrén, E. E. BAnGs, E. H. BrAdley, J. A. fontAine, W. HAll, Y. ilioPoulos, M. D. jiMenez, E. A. jozwiAk, O. liBerG, C. M. MACk, T. J. Meier, C. C. nieMeyer, H. C. Pedersen, H. sAnd, R. N. sCHultz, D. W. sMitH, P. wABAkken, and A. P. wydeven. 2008. The effects of breeder loss on wolves. Journal of Wildlife Management 72:89–98. (B.C. flnro) British Columbia Ministry of Forests, Lands and Natural Resource Operations. 2014. Management plan for the grey wolf (Canis lupus) in British Columbia. Government of B.C., Victoria. (B.C. flnro) British Columbia Ministry of Forests, Lands and Natural Resource Operations. 2015. Provincial Framework for Moose Management in British Columbia. B.C. Ministry of Forests, Lands, and Natural Resource Operations, Victoria. CAlenGe, C. 2006. The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516–519. CArstensen, M., E. C. HildeBrAnd, D. PlAttner, M. dexter, A. wünsCHMAnn, and A. ArMien. 2017. Causes of non-hunting mortality of adult moose in Minnesota, 2013–2017. Minnesota Department of Natural Resources. ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 37 CAriAPPA, C. A., J. K. oAkleAf, W. B. BAllArd, and S. W. BreCk. 2011. A reappraisal of the evidence for regula- tion of wolf populations. Journal of Wildlife Management 75:726–730. ClArk, T. J., J. S. Horne, M. HeBBlewHite, and A. D. luis. 2021. Stochastic preda- tion exposes prey to predator pits and local extinction. Oikos 130:300–309. Cook, R. C., J. oyster, K. MAnsfield, and R. B. HArris. 2021. Evidence of sum- mer nutritional limitations in a north- eastern Washington moose population. Alces 57:23–46. ECCC 2024 (Environment and Climate Change Canada). 2024. Historical data. https://climate.weather.gc.ca/historical_ data/search_historic_data_e.html frAnCis, A. L., C. ProCter, G. kuzyk, and J. T. fisHer. 2021. Female moose prioritize forage over mortality risk in harvested landscapes. The Journal of Wildlife Management 85:156–168. fuller, T. K. 1989. Population dynamics of wolves in north-central Minnesota. Wildlife Monographs 105:1–41. fuller, T. K., and L. B. keitH. 1980. Wolf population dynamics and prey relation- ships in northeastern Alberta. The Journal of Wildlife Management 44:583–606. fuller, T. K., L. D. MeCH, and J. F. CoCHrAne. 2003. Wolf population dynamics. Pages 161-191 in L. D. Mech and L. Boitani (ed.) Wolves: behavior, Ecology and Conservation. University of Chicago Press, Chicago, IL. GABle, T. D., S. K. windels, J. G. BruGGink, and A. T. HoMkes. 2016. Where and how wolves (Canis lupus) kill beavers (Castor canadensis). PLoS ONE 11:e0165537 GABle, T. D., S. K. windels, M. C. roMAnski, and F. rosell. 2018. The forgotten prey of an iconic predator: a review of inter- actions between grey wolves Canis lupus and beavers Castor spp. Mammal Reviews 48:123–138. GAsAwAy, W. C., R. D. Boertje, D. V. GrAnGAArd, D. G. kelleyHouse, R. O. stePHenson, and D. G. lArsen. 1992. The role of predation in limiting moose at low densities in Alaska and Yukon and implications for conservation. Wildlife Monographs 120:1–59. GAsAwAy, W. C., R. O. stePHenson, J. L. Davis, P. E. K. Shepherd, and O. E. Burris. 1983. Interrelationships of wolves, prey, and man in interior Alaska. Wildlife Monographs 84:1–50. GillinGHAM, M. 2009. Documentation for using Find Points Cluster Identification Program. University of Northern British Columbia, Prince George, B.C. Gorley, R. A. 2016. A strategy to help restore moose populations in British Columbia: Recommendations. Triangle Resources Inc., Victoria, B.C. HAyes, R. D., A. M. BAer, U. wotsCHikowsky, and A. S. HArestAd. 2000. Kill rate by wolves on moose in the Yukon. Canadian Journal of Zoology 78:49–59. HAyes, R. D., R. fArnell, R. M. P. wArd, J. CArey, M. deHn, G. W. kuzyk, A. M. BAer, C. L. Gardner, and M. o’donoGHue. 2003. Experimental reduction of wolves in the Yukon: Ungulate responses and management implications. Wildlife Monographs 152:1–35. HAyes, R. D., and A. S. HArestAd. 2000 Demography of a recovering wolf popu- lation in the Yukon. Canadian Journal of Zoology 78:36–48. jedrzejewski, W., K. sCHMidt, J. tHeuerkAuf, B. jedrzejewskA, B. and H. okArMA. 2001. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza Primeval Forest in Poland. Canadian Journal of Zoology 79:1993–2004. kACzensky, P., R. D. HAyes, and C. ProMBerGer. 2005. Effect of raven Corvus corax scavenging on the kill rates of wolf Canis lupus packs. Wildlife Society Bulletin 11:101–108. klACzek, M., S. MArsHAll, A. BAtHo, and M. Anderson. 2017. Density and https://climate.weather.gc.ca/historical_data/search_historic_data_e.html https://climate.weather.gc.ca/historical_data/search_historic_data_e.html WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 38 abundance of moose (Alces alces) within the southern Omineca Region, central British Columbia. B.C. Ministry of Forests, Lands, and Natural Resource Operations, Prince George. koetke, L. J., 2024. Moose responses to anthro- pogenic disturbance across a range of spa- tial scales: Diet, habitat use, and movement. Ph.D. thesis, University of Northern British Columbia, Prince George. kuzyk, G. W. 2016. Provincial population and harvest estimates of moose in British Columbia. Alces 52:1–11. kuzyk, G. W., and I. W. HAtter. 2014. Using ungulate biomass to estimate abundance of wolves in British Columbia. Wildlife Society Bulletin 38:878–883. kuzyk, G. W., I. HAtter, S. MArsHAll, C. ProCter, B. CAdsAnd, D. lirette, H. sCHindler, M. BridGer, P. stent, A. wAlker, and M. klACzek. 2018. Moose population dynamics during 20 years of declining harvest in British Columbia. Alces 54:101–119. kuzyk, G., and D. HeArd. 2014. Research design to determine factors affecting moose population change in British Columbia: testing the landscape change hypothesis. B.C. Ministry of Forests, Lands and Natural Resource Operations, Wildlife Bulletin No. B-126. kuzyk, G., C. ProCter, S. MArsHAll, and D. Hodder. 2019. Factors affecting moose population declines in British Columbia: Updated Research Design. B.C. Ministry of Forests, Lands and Natural Resource Operations, Wildlife Bulletin No. B-128. kuzyk, G. W., K. D. sCHurMAnn, S. M. MArsHAll, and C. ProCter. 2020. Assessing the are of harvested moose prior to population declines in British Columbia. Alces 56:97–106. lAMoureux, J. L., S. D. fitzGerAld, M. K. CHurCH, and D. W. AGnew. 2011. The effect of environmental storage condi- tions on bone marrow fat determination in three species. Journal of Veterinary Diagnostic Investigation 23:312–315. MACnulty, D. R., D. W. sMitH, L. D. MeCH, J. A. vuCetiCH, and C. PACker. 2012. Nonlinear effects of group size on the success of wolves hunting elk. Behavioral Ecology 23:75–82. MeCH, L. D. 2008. Precision of descriptors for percent marrow fat content in white- tailed deer, Odocoileus virginianus. Canadian Field Naturalist 122:273. MeCH, L. D., and L. BoitAni. 2003. Wolf social ecology. Pages 1-34 in L. D. Mech and L. Boitani (ed.) Wolves: behavior, Ecology and Conservation. University of Chicago Press, Chicago, IL. MeCH, L. D., T. J. Meier, J. W. BurCH, and L. G. AdAMs. 1995. Patterns of prey selec- tion by wolves in Denali National Park, Alaska. Pages 231–243 in L. N. Carbyn, S. H. Fritts, and D. R. Seip (ed.) Ecology and conservation of wolves in a changing world. Canadian Circumpolar Institute, Edmonton, AB. MeCH, L. D., and R. O. Peterson. 2003. Wolf- prey relations. Pages 131-160 in L. D. Mech and L. Boitani (ed.) Wolves: behav- ior, Ecology and Conservation. University of Chicago Press, Chicago, IL. MeCH, L. D., and G. D. delGiudiCe. 1985. Limitations of the marrow-fat technique as an indicator of condition. Wildlife Society Bulletin 13:204–206. MeidinGer, D. and J. PojAr. 1991. Ecosystems of British Columbia. B.C. Ministry of Forests, Victoria, B.C. Special Report Series 66. Messier, F. 1994. Ungulate population mod- els with predation: a case study with the North American moose. Ecology 75:478–488. Metz, M. C., J. A. vuCetiCH, D. W. sMitH, D. R. stAHler, and R. O. Peterson. 2011. Effect of sociality and season on gray wolf (Canis lupus) foraging behavior: implications for estimating summer kill rate. PLoS ONE 6:e17332. Metz, M. C., D. W. sMitH, J. A. vuCetiCH, D. R. stAHler, and R. O. Peterson. 2012. Seasonal patterns of predation for gray ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 39 wolves in the multi-prey system of Yellowstone National Park. Journal of Animal Ecology 81:553–563. (MN DNR) Minnesota Department of Natural Resources. 2001. Minnesota Wolf Management Plan. Minnesota Department of Natural Resources. MoreHouse, A. T., and M. S. BoyCe. 2011. From venison to beef: seasonal changes in wolf diet composition in a livestock grazing landscape. Frontiers in Ecology and the Environment 9:440-445. MowAt, G., L. vAndervennen, M. Anderson, M. BridGer, S. wHite, S. MArsHAll, K. MACAulAy, and S. o’donovAn. 2022. An evaluation of the accuracy of licensed wolf harvest data and the correlation with population trends in British Columbia. B.C. Ministry of Water, Land, and Resource Stewardship, Technical Report 27. MuMMA, M., and M. GillinGHAM. 2019. Determining factors that affect survival of moose in Central British Columbia. Technical report to the Habitat Conservation Trust Foundation for Grant Agreement CAT19-0-522, Prince George, B.C. MuMMA, M., A., M. P. GillinGHAM, S. MArsHAll, C. ProCter, A. R. BevinGton, and M. sCHeideMAn. 2021. Regional moose (Alces alces) responses to for- estry cutblocks are driven by land- scape-scale patterns of vegetation composition and regrowth. Forest Ecology and Management 481:118763. Murden, D., J. HunnAM, B. De Groef, G. rAwlin, and C. MCCowAn. 2017. Comparison of methodologies in deter- mining bone marrow fat percentage under different environmental conditions: assessing a tool for ruminant welfare investigations. Journal of Veterinary Diagnostic Investigation 29:83-90. PAlM, D. 2001. Prey selection, kill and con- sumption rates of moose by wolves in cen- tral Sweden, comparison to moose population and human harvest. Thesis. Sveriges Lantbruks Universitet, Uppsala, Sweden. PAquet, P. C., and L. N. CArByn. 2003. Gray wolf (Canis lupus) and allies. Pages 482- 510 in G. A. Feldhammer, B. C. Thompson, J. A. Chapman (ed.) Wild Mammals of North America: Biology, Management, and Conservation, 2nd ed. John Hopkins University Press, Baltimore, MD. PAtterson, B. R., J. F. Benson, K. R. Middel, K. J. Mills, A. Silver, and M. E. Obbard. 2013. Moose calf mortality in central Ontario, Canada. Journal of Wildlife Management 77:832–841. Peterson, R. O., J. D. woolinGton, and T. N. Bailey. 1984. Wolves of the Kenai Peninsula, Alaska. Wildlife Monographs 88:3–52. ProCter, C., M. Anderson, M. sCHeideMAn, S. MArsHAll, H. sCHindler, H. sCHwAntje, D. Hodder, and E. BlytHe. 2020. Factors affecting moose popula- tion declines in British Columbia, 2020 Progress report: Feb 2012-May 2020. B.C. Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Wildlife Working Report No. WR-128. quAyle, J. F. A. G. MACHutCHon, and D. N. Jury. 2001. Modelling moose sightabil- ity in south-central British Columbia. Alces 37:43–54. R Core teAM. 2024. R: A language and envi- ronment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. reGelin, W. L., P. vAlkenBerG, and R. D. Boertje. 2005. Management of large predators in Alaska. Wildlife Biology in Practice 17:77–85. sAnd, H., B. ziMMerMAn, P. wABAkken, H. Andrèn, and H. C. Pedersen. 2005. Using GPS technology and GIS cluster analyses to estimate kill rates in wolf-un- gulate ecosystems. Wildlife Society Bulletin 33:914–925. sCHeideMAn, M. 2018. Use and selection at two spatial scales by female moose (Alces WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 40 alces) across central British Columbia fol- lowing a mountain pine beetle outbreak. M.Sc. thesis. University of Northern British Columbia, Prince George. sCHeideMAn, M., and M. Anderson. 2022. Evaluating elk population trend, habitat use, and potential for competition with moose. British Columbia Ministry of Forests, Prince George. sCHeideMAn, M., and M. Anderson. 2023. Vanderhoof aerial minimum total count elk survey, central British Columbia 2021-22. British Columbia Ministry of Forests, Prince George. sCHeideMAn, M., M. Anderson, and M. Klaczek. 2021. Density and composition of moose (Alces alces) in the southern Omineca, central British Columbia. British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George sCHeideMAn, M., M. Anderson, and K. MACAulAy. 2024. Vanderhoof aerial minimum total count elk survey, central British Columbia, 2023–24. British Columbia Ministry of Water, Land and Resource Stewardship, Prince George. sells, S. N., M. S. MitCHell, K. M. Podruzny, D. E. AusBAnd, D. J. eMlen, J. A. Gude, T. D. sMuCker, D. K. Boyd, and K. E. loonAM. 2022. Competition, prey, and mortalities influence gray wolf group size. Journal of Wildlife Management 86:e22193. sikes, R.S., and tHe AniMAl CAre And use CoMMittee of tHe AMeriCAn soCiety of MAMMAloGists. 2016. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. Journal of Mammalogy 97:663–688. sMitH, D. W., E. E. BAnGs, J. K. oAkleAf, C. MACk, J. fontAine, D. Boyd, M. jiMenez, D. H. PletsCHer, C. C. nieMeyer, D. R. stAHler, J. HolyAn, V. J. AsHer, and D. L. MurrAy. 2010. Survival of colonizing wolves in the northern Rocky Mountains of the United States, 1982–2004. Journal of Wildlife Management 74:620–634. sPeArs, B. L., W. J. Peterson and W. B. BAllArd. 2003. Bone marrow fat content from moose in northeastern Minnesota, 1972-2000. Alces 39:273–285. vuCetiCH, J. A., M. HeBBlewHite, D. W. sMitH, and R. O. Peterson. 2011. Predicting prey population dynamics from kill rate, predation rate and preda- tor-prey ratios in three wolf-ungulate systems. Journal of Animal Ecology 80:1236–1245. vuCetiCH, J. A., R. O. Peterson, and T. A. wAite. 2004. Raven scavenging favours group foraging in wolves. Animal Behaviour 67:1117–1126. weBB, N.F., M. HeBBlewHite, and E. H. Merrill. 2008. Statistical methods for identifying wolf kill sites using Global Positioning System locations. Journal of Wildlife Management 72:798–807. wHite, G. C., and R. A. GArrott. 1990. Home range estimation. Pages 145-182 in G. C. White and R. A. Garrott (ed.) Analysis of Wildlife Radio-tracking Data. Academic Press, Cambridge, MA. ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 41 A PP E N D IC E S A pp en di x 1. C an di da te lo gi st ic re gr es si on m od el s b y se as on a nd st ud y ar ea p re di ct in g la rg e pr ey k ill si te s b y w ol ve s i n Pr in ce G eo rg e So ut h (P G S) a nd Jo hn Pr in ce R es ea rc h Fo re st (J PR F) s tu dy a re as in n or th -c en tra l B .C ., 20 18 -2 02 2. T op a nd e qu iv al en t m od el s sh ow n. P re di ct or v ar ia bl es a re m ea n di st an ce o f po in ts in c lu st er to th e cl us te r c en tro id (A v_ D is t), n um be r o f l oc at io ns in c lu st er (N um _P ts ) a nd n um be r o f d ay s b et w ee n fir st a nd la st v is its to c lu st er (D ay s) . In di vi du al w ol f w as a ra nd om e ffe ct in a ll m od el s. A st er ix d en ot es st at is tic al si gn ifi ca nc e at P < 0 .0 5. St ud y A re a an d se as on C an di da te m od el N um _P ts C oe ffi ci en t Av _D is t C oe ffi ci en t D ay s C oe ffi ci en t A IC c ΔA IC c PG S+ JP R F; a ll se as on s N um _P ts + D ay s 0. 03 26 * -0 .0 10 59 8* 11 82 .4 1. 9 N um _P ts + D ay s + A v_ D is t 0. 03 23 * 0. 00 24 -0 .0 11 1* 11 80 .5 0. 0 PG S al l s ea so ns N um _P ts + D ay s 0. 03 83 * -0 .0 10 2* 92 4. 0 2. 1 N um _P ts + D ay s + A v_ D is t 0. 03 76 * 0. 00 36 -0 .0 11 0* 92 1. 9 0. 0 JP R F al l s ea so ns N um _P ts + A v_ D is t 0. 02 23 * -0 .0 12 3 25 4. 6 0. 7 N um _P ts 0. 02 12 * 25 4. 4 0. 4 N um _P ts + D ay s 0. 02 31 * -0 .0 13 0 25 4. 0 0. 0 N um _P ts + D ay s + A v_ D is t 0. 02 36 * -0 .0 09 3 -0 .0 10 9 25 5. 1 1. 1 PG S+ JP R F; w in te r N um _P ts + A v_ D is t 0. 06 58 * -0 .0 02 0 43 1. 2 1. 1 N um _P ts + D ay s 0. 06 84 * -0 .0 10 2 43 2. 5 2. 4 N um _P ts + D ay s + A v_ D is t 0. 06 80 * 0. 00 47 -0 .0 11 2 43 0. 1 0. 0 PG S w in te r N um _P ts + A v_ D is t 0. 06 57 * 0. 00 60 35 9. 6 0. 0 N um _P ts + D ay s + A v_ D is t 0. 06 74 * 0. 00 81 35 9. 7 0. 1 JP R F w in te r N um _P ts + A v_ D is t 0. 08 87 * -0 .0 43 3 66 .8 0 N um _P ts 0. 07 20 * 67 .4 0. 6 N um _P ts + D ay s 0. 08 32 * -0 .0 33 5 66 .9 0. 1 N um _P ts + D ay s + A v_ D is t 0. 09 62 * -0 .0 36 6 -0 .0 29 2 67 .3 0. 5 PG S+ JP R F; su m m er N um _P ts + D ay s 0. 01 91 * -0 .0 10 9* 68 4. 0 0. 0 N um _P ts + D ay s + A v_ D is t 0. 01 84 * 0. 00 53 -0 .0 12 0* 68 5. 1 1. 1 PG S su m m er N um _P ts + D ay s 0. 02 59 * -0 .0 13 4* 51 4. 8 0 N um _P ts + D ay s + A v_ D is t 0. 02 42 * 0. 00 82 -0 .0 15 1* 51 5. 3 0. 5 JP R F su m m er N um _P ts + A v_ D is t 0. 00 89 * -0 .0 14 7 16 9. 2 0. 5 N um _P ts 0. 00 86 * 16 8. 7 0 N um _P ts + D ay s 0. 00 97 * -0 .0 08 2 17 0. 0 1. 3 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA ALCES VOL. 60, 2024 42 A pp en di x 2. C an di da te lo gi st ic re gr es si on m od el s b y se as on an d st ud y ar ea p re di ct in g m oo se k ill si te s b y w ol ve s i n Pr in ce G eo rg e S ou th (P G S) an d Jo hn P rin ce R es ea rc h Fo re st (J PR F) s tu dy a re as in n or th -c en tra l B .C ., 20 18 -2 02 2. T op a nd e qu iv al en t m od el s sh ow n. P re di ct or v ar ia bl es a re m ea n di st an ce o f p oi nt s in cl us te r to t he c lu st er c en tro id ( Av _D is t), n um be r of l oc at io ns i n cl us te r (N um _P ts ) an d nu m be r of d ay s be tw ee n fir st a nd l as t vi si ts t o cl us te r (D ay s) . In di vi du al w ol f w as a ra nd om e ffe ct in a ll m od el s. A st er ix d en ot es st at is tic al si gn ifi ca nc e at P < 0 .0 5. PG S+ JP R F; a ll se as on s N um _P ts + D ay s 0. 03 03 * -0 .0 12 6* 10 99 .6 2. 0 N um _P ts + D ay s + A v_ D is t 0. 03 07 * -0 .0 02 8 -0 .0 12 0* 10 97 .6 0. 0 PG S al l s ea so ns N um _P ts + D ay s 0. 03 59 * -0 .0 12 2* 87 6. 8 1. 9 N um _P ts + D ay s + A v_ D is t 0. 03 67 * -0 .0 02 9 -0 .0 11 7* 87 4. 9 0. 0 JP R F al l s ea so ns N um _P ts + A v_ D is t 0. 01 95 * -0 .0 13 2 21 5. 2 0. 7 N um _P ts 0. 01 89 * 21 4. 8 0. 3 N um _P ts + D ay s 0. 02 08 * -0 .0 14 2 21 4. 5 0. 0 N um _P ts + D ay s + A v_ D is t 0. 02 11 * -0 .0 10 4 -0 .0 12 4 21 5. 6 1. 1 PG S+ JP R F; w in te r N um _P ts + A v_ D is t 0. 06 11 * -0 .0 04 2 42 1. 3 0. 2 N um _P ts + D ay s 0. 06 22 * -0 .0 10 2 42 3. 0 2. 0 N um _P ts + D ay s + A v_ D is t 0. 06 29 * -0 .0 01 8 -0 .0 09 9 42 1. 1 0. 0 PG S w in te r N um _P ts + A v_ D is t 0. 06 08 * -0 .0 01 3 35 5. 5 0. 0 N um _P ts 0. 06 01 * 35 7. 1 1. 6 N um _P ts + D ay s 0. 06 26 * -0 .0 09 6 35 7. 1 1. 6 N um _P ts + D ay s + A v_ D is t 0. 06 29 * 0. 00 09 -0 .0 09 8 35 5. 5 0. 0 JP R F w in te r N um _P ts + A v_ D is t 0. 08 18 * -0 .0 42 9 60 .9 0. 0 N um _P ts 0. 06 83 * 61 .0 0. 1 N um _P ts + D ay s 0. 07 18 * -0 .0 18 6 62 .4 1. 5 N um _P ts + D ay s + A v_ D is t 0. 08 30 * -0 .0 39 7 -0 .0 11 9 62 .8 1. 9 PG S+ JP R F; su m m er N um _P ts + D ay s 0. 01 75 * -0 .0 13 7* 60 4. 9 0. 0 N um _P ts + D ay s + A v_ D is t 0. 01 71 * 0. 00 36 -0 .0 14 5* 60 6. 6 1. 7 (C on tin ue d) ALCES VOL. 60, 2024 WOLF PREDATION ON MOOSE IN BRITISH COLUMBIA 43 A pp en di x 2. (C on tin ue d) C an di da te lo gi st ic re gr es si on m od el s by s ea so n an d st ud y ar ea p re di ct in g m oo se k ill s ite s by w ol ve s in P rin ce G eo rg e So ut h (P G S) an d Jo hn P rin ce R es ea rc h Fo re st ( JP R F) s tu dy a re as in n or th -c en tra l B .C ., 20 18 -2 02 2. T op a nd e qu iv al en t m od el s sh ow n. P re di ct or v ar ia bl es a re m ea n di st an ce o f p oi nt s in c lu st er to th e cl us te r c en tro id (A v_ D is t), n um be r o f l oc at io ns in c lu st er (N um _P ts ) a nd n um be r o f d ay s be tw ee n fir st a nd la st v is its to cl us te r ( D ay s) . I nd iv id ua l w ol f w as a ra nd om e ffe ct in a ll m od el s. A st er ix d en ot es st at is tic al si gn ifi ca nc e at P < 0 .0 5. PG S su m m er N um _P ts + D ay s 0. 02 56 * -0 .0 16 5* 46 7. 2 0. 0 N um _P ts + D ay s + A v_ D is t 0. 02 54 * 0. 00 11 -0 .0 16 8* 46 9. 2 2. 0 JP R F su m m er N um _P ts + A v_ D is t 0. 00 62 -0 .0 20 5 13 2. 9 0. 4 N um _P ts 0. 00 63 13 2. 5 0. 0 Av _D is t -0 .0 21 6 13 4. 0 1. 5 N um _P ts + D ay s 0. 00 81 -0 .0 13 4 13 3. 0 0. 5 _Hlk183266892 _Hlk193461973 _Hlk183327537 _Hlk183526788 _Hlk183513684 _Hlk193462461 _Hlk193463255 _Hlk193462591 _Hlk193618390 _Hlk183326120 _Hlk183525768 _Hlk183273559 _Hlk183525586 _Hlk193015107 _Hlk193616408 _Hlk193695220 _Hlk183330077 _Hlk193618052 _Hlk183148038 _Hlk193618234 _Hlk193693141 _Hlk183266588