45 SPATIAL AND TEMPORAL OCCURRENCES OF PRAIRIE MOOSE ACROSS AN URBAN TO RURAL GRADIENT IN SASKATOON, CANADA Kaitlyn E. Harris1, and Ryan K. Brook1 1Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, 51 Campus Drive, Saskatoon, Saskatchewan, Canada, S7N 5A8, keh731@usask.ca ABSTRACT: The geographic range and abundance of North American moose (Alces alces) has varied over recent decades, with contractions in areas, recolonization of historical habitats and dispersals into new areas and, notably, increased moose sightings near developed, urban areas. The City of Saskatoon, Saskatchewan is located in semi-arid, open prairies and surrounded by high-intensity agriculture. Despite being atypical moose habitat, Saskatoon has experienced an increased frequency of moose observations at the urban-rural interface over the last several decades. We characterized spatial and temporal patterns in moose detections over the course of three study years (September 2020–September 2023) using data from 29 trail cameras distributed along an urban to rural gradient within the city boundary of Saskatoon. We quantified moose detections (n = 60) from photos (n = 249) collected at 12 of the camera trap sites. We detected moose each year of the study (8, 23, and 29 detections in years 1, 2, and 3, respectively). Moose detections were highest overall in July (n = 15) and at rural sites adjacent to the city (n = 47). Using generalized linear modeling, we found that moose were negatively associated with urban sites containing higher proportions of development (>50% impervious surface cover), and that detections occurred most frequently at night when darkness provided hiding cover and human activity was lowest. We provide suggestions and recommendations for future urban moose research and management. ALCES VOL. 60: 45–57 (2024) Key Words: Alces alces, detection, development, gradient, prairies, Saskatchewan, trail camera, urban-rural Moose have long been recognized as a cold-adapted boreal forest specialist with a broad circumpolar geographic range that incorporates much of northern Canada (Teitelbaum et al. 2021), including the Taiga Plains, Hudson Plains, Boreal Plains, Boreal Shield, Boreal Cordillera, Taiga Shield, and Taiga Cordillera Ecozones (Pastor et al. 1988, Wilken et al. 1996, Jensen et al. 2018). Although populations and range dynamics of moose have fluctuated over time, Timmermann and Rodgers (2017) found that of the 12 Canadian jurisdictions with moose populations, 3 remained stable (Yukon, British Columbia, Nova Scotia), 2 were increasing (Quebec, New Brunswick), 5 were decreasing (Alberta, Saskatchewan, Manitoba, Ontario, Newfoundland), and 2 had no available estimates (Northwest Territories, Nunavut). Despite forage avail- ability being one of the primary limitations to moose populations (Tape et al. 2016), warm- ing climates have led to longer growing sea- sons and increased primary productivity that have contributed to a northern range expan- sion (Zhou et al. 2022). At the southern extent, climate change has been hypothe- sized to be a limitation due to heat stress (Wattles et al. 2018a, DeCesare et al. 2024). However, while some southern populations mailto:keh731@usask.ca URBAN TO RURAL PRAIRIE MOOSE ALCES VOL. 60, 2024 46 have been declining, many others have actively expanded into areas that experience daily ambient temperatures far above the reported tolerance range. This is thought to be explained by behavioural adaptations for reducing thermal heat stress, such as shifting away from the preferential selection of regenerating forest habitats towards forested wetlands and coniferous forest (Wattles et al. 2018a), along with other potential factors such as absence of primary predators. Logging may positively influence popu- lations through the development of early successional habitats that are favorable for moose (Anderson et al. 2017, Ordiz et al. 2021). However, because moose have shown strong negative responses to both high- density road infrastructure and high road use intensity (Wattles et al. 2018b), transporta- tion infrastructure may limit the distribution of moose and their use of available habitat patches. The risk of moose-vehicle colli- sions also increases with increasing road density and human activity (Joyce and Mahoney 2001), and this risk is highest during crepuscular times and during the sea- sons associated with increased moose move- ment (e.g., spring dispersal, fall rut; Laliberté and St-Laurent 2020). Non-lethal human disturbance, such as off-trail hiking or snowmobile use, has prompted avoidance behaviour in moose although typically only for a short duration when at low to moderate frequencies (Neumann et al. 2011). The potential establishment of “farmland moose” populations, characterized as living primarily on agricultural-dominated lands (Laforge et al. 2016), has several important socio-economic implications. Moose feeding activities and trampling can detrimentally impact ecosystems, leading to altered vege- tation communities (Persson et al. 2000). The substantial habitat overlap among wild ungulates in the prairies has also increased the risk for the transmission and spread of infectious diseases (Weiskopf et al. 2019). Furthermore, moose can cause considerable damage to agricultural crops, fence lines, and machinery (Laforge et al. 2017b, Tranulis and Tryland 2023), which may harm human livelihood within agriculture-dominated regions such Saskatchewan. This may neces- sitate increased spending on compensation measures for affected landowners (Hanbury- Brown et al. 2021), or the implementation of a harvest process to help manage the popula- tion through increased annual hunting oppor- tunities (Timmermann and Rodgers 2005). Wildlife and resource managers would benefit from a contemporary understanding of moose activity on an urban mixed prairie and planted tree landscape embedded within an extensive agriculture-dominated land- scape. Using a network of wildlife monitor- ing trail cameras distributed within the city boundary of Saskatoon, Saskatchewan, Canada, our objectives were to: 1) quantify moose detections along an urban to rural gradient, 2) relate spatial patterns of detec- tions to vegetation canopy cover and height, and to the proportion of impervious surface cover (e.g., roads, buildings, parking lots) at each site, and 3) identify annual, monthly, and daily trends in moose detections. STUDY AREA We defined the study area as the city bound- ary of Saskatoon, Saskatchewan, Canada (Fig. 1), which had a population of 266,000 residents and a land area of 227 km2 (Statistics Canada 2021). Saskatoon is situated in the Prairie Ecozone of Canada, which has a semi-arid climate, and experienced summer temperatures highs in July (mean daily tem- perature: 19o C) and winter lows in January (mean daily temperature: -21o C; Government of Canada 2024). Saskatchewan contained approximately 24.5 million hectares of total farm area, with 67% designated as cropland (St. Pierre and Mhlanga 2022). The dominant ALCES VOL. 60, 2024 URBAN TO RURAL PRAIRIE MOOSE 47 farmland crop was canola (30% of total crop- land), followed by spring wheat (18%), durum wheat (12%), lentils (9%), and barley (9%). Saskatchewan is also included in the Prairie Pothole Region (PPR) of Canada and contains thousands of small wetland pot- holes embedded within the complex agro- ecosystem (Pennock et al. 2010). Wetlands in the PPR are often ringed by deciduous trees and shrubs that provide ungulates with opportunities for forage, cover, shelter, and thermoregulation through submersion in standing water (Laforge et al. 2016). The moose population in Saskatchewan was estimated as 45,516 in 2000, with a mean density of 0.29 moose/km2, and most (69%) of the population supported by the primary range within the Boreal Plains Ecozone (Arsenault 2000). This range expe- rienced a decline in moose population den- sity averaging 30% since 2000 (Arsenault et al. 2019). The agricultural secondary range consisted of pockets of suitable habitat scat- tered across the Prairie Ecozone, south of the Boreal Plain. This region contained approxi- mately 2% of the provincial population (Arsenault 2000) and was considered unable to sustain high-density populations due to high-intensity agriculture, human settle- ment, and higher ambient temperatures. The City of Saskatoon was within the Saskatoon Wildlife Management Zone (WMZ SMZ) that had a resident-only draw moose hunting season in October and November for individuals to harvest one moose of either sex (rifle hunting not Fig. 1. The location of the study area relative to the Boreal Shield, Boreal Plains, and Prairie Ecozones, and the distribution of wildlife monitoring trail cameras (n = 29) deployed in a stratified random distribution along an urban to rural gradient within the city boundary of Saskatoon, Saskatchewan, Canada, 2020-2023. URBAN TO RURAL PRAIRIE MOOSE ALCES VOL. 60, 2024 48 permitted). The 2023 Hunter Harvest Survey estimated 10 moose harvested in this zone (approximately 0.002 moose/ km2; Government of Saskatchewan 2024b). Indigenous subsistence harvest of moose was legal throughout the year, but nothing is currently known about past or current harvest levels. Population esti- mates within Saskatoon are unknown, but media reports have documented numer- ous sporadic occurrences of moose throughout Saskatoon, including well within urban environments (Global News 2020, 2021, 2022, CKOM News 2024). METHODS We quantified moose detections using data from a larger, comprehensive multi-species trail camera study to monitor wild mammal distribution and diversity in Saskatoon that was designed in accordance with the proto- cols provided by the Urban Wildlife Information Network (UWIN; Magle et al. 2019). This minimally invasive research was exempt from University of Saskatchewan Animal Research Ethics Board approval. Camera trap locations were determined by stratifying Saskatoon into 6 primary land cover classifications (agricultural, aquatic, built (i.e., impervious surface area), forested and shrubland, grassland, and greenspace (i.e., parks, golf courses)) using the Meewasin Valley Authority Natural Areas Inventory land cover map (Hooey 2021). We created 2 perpendicular transects (North— South, 16 km; East—West, 20 km) originat- ing from a central downtown location (52o7’24” N 106o39’50” W) and extending outwards to the city boundary, using ArcGIS Pro (v. 10.7.1, ESRI Inc. 2019). We used stratified quasi-random sampling to locate 5 camera traps within each of the 6 land cover classifications (n = 30), enforcing 1 km min- imum distance between points that were within 1 km of either side of the transect. We quantified the proportions of each identified land cover within a 500 m radius buffer zone surrounding each camera trap. Areas with incomplete land cover data were assigned an initial value of “null” and visu- ally quantified using City of Saskatoon orthoimagery. The landcover classification was further used to build the urban-rural gra- dient by identifying unique categories of development within the landscape of our study area, based on the level of human development. We defined human develop- ment by a measure of built land cover, which identified the proportions of impervious sur- face area (i.e., developed surface cover such as roads, buildings, parking lots) within each zone. Camera sites were sorted into 3 devel- opment classes: rural, peri-urban, or urban. We defined rural zones as those containing < 25% impervious surface area, peri-urban zones as containing 25-50% impervious sur- face area, and urban zones as containing > 50% impervious surface area. Our camera trap sample had 9 cameras (30%) within rural areas, 9 (30%) within peri-urban areas, and 12 (40%) within urban areas. Within each zone, 4 potential camera trap locations were selected based on accessibility and suitability of the location for year-round monitoring and likelihood of mammal encounters (e.g., proximity to water source, natural trails or paths). We used ground truthing and landowner discussions to final- ize camera trap placements (1 camera per zone). Data Collection We used Browning Dark Ops HD Pro X motion-activated infrared trail cameras on their default settings (trail mode with high detectability) and set to obtain a burst of three images at 1 second intervals each time it was motion-triggered and a 30 second minimum delay between trigger events. We deployed cameras from 8 September 2020 to ALCES VOL. 60, 2024 URBAN TO RURAL PRAIRIE MOOSE 49 9 September 2023. We positioned cameras 1.5-2 m above the ground and, where possi- ble, facing a potential travel corridor (i.e., an area of land such as a riverbank or ditch that connects habitat patches) or trail (e.g., foot trail, deer path) with minimal vegetation in front to reduce accidental triggering. Camera traps operated continuously year-round, and were serviced every 1-4 weeks by research staff. We retrieved and replaced camera cards during each visit. Over the 3-year study period, 14 cameras on 9 sites were found temporarily inoperable and replaced, limiting data loss to <15 total days per site. However, we censored from analyses data from 1 site with repeated camera failures. We tagged each photo with metadata (i.e., species data, image time and date, camera number, site identifier, camera GPS location coordinates), and removed data that might identify individual people. At each site we estimated the green canopy cover (%) above each camera using the Canopeo application (Patrignani and Ochsner 2015). We calcu- lated the mean height of vegetation at each site based on field measurements collected during peak greenness in July of the highest point of herbaceous vegetation below and 10 m in front of each camera. We quantified moose “detections” and “events” for each camera trap. We defined a “detection” as any time a camera took 1 ini- tial photo of a moose, and an “event” included all moose photos consecutively captured by the same camera within a 1-hour timeframe (Laforge et al. 2017a). Most events (75%) were < 1 minute in duration and only one event exceeded 10 minutes. Any group of images within a 1-hour event were assumed to be the same moose (counted as n = 1 detection for that event) unless we could confidently distinguish a unique indi- vidual through identifiable features (e.g., n = 2 detections for that event). We documented the year, month, time-of-day, and dominant landcover type associated with each event. The time-of-day classification was standard- ized based on hours of daylight to account for the annual fluctuations in Saskatoon daylength. Hours of daylight were calcu- lated as the total difference in hours between the true sunrise and sunset times (obtained from: timeanddate.com) for each event. We defined events during the first 25% of day- light hours as morning, the following 50% of daylight hours as afternoon, the final 25% of daylight hours as evening, and after sunset or before sunrise as night. Where possible, moose were classified by age and sex through visual examination of photographs using key identifier characteristics. We con- sidered moose adults at one year of age. Statistical Analysis We employed generalized linear modelling (GLM) to assess the significance of detec- tion covariates using program R (v. 4.2.1, R Core Team 2024), with a negative binomial distribution (Package: “MASS”) of the response variable (Ver Hoef and Boveng 2007). Models contained temporal predictor variables (month and time-of-day) and habi- tat categories (green canopy cover, mean vegetation height, dominant landcover class, development class). We employed a step- wise selection process to compare candidate models, removing non-significant predictors one at a time using Akaike’s Information Criterion (AIC) and a likelihood ratio test. The best fit model was selected when removal of any predictor variable reduced model performance based on higher AIC scores. Because one camera site accounted for 57% of all moose detections, we conducted a separate analysis with and without this out- lier. We report in Results the analysis exclud- ing the outlier and present the results of the model including this site in Supporting Material. http://timeanddate.com URBAN TO RURAL PRAIRIE MOOSE ALCES VOL. 60, 2024 50 RESULTS We collected a total of 249 moose photos at 12 of 29 camera traps, comprising 53 unique moose events and 60 moose detections (Fig 2). Overall, 13% of events contained 2 moose and the remainder contained what appeared to be a single individual. The mean number of photos per event was 4.7 (minimum = 1, maximum = 27). Among events, 17% were of a single photograph (i.e., event length = 0:00 minutes). Of the events with > 1 photograph, 68% were < 1 minute in length and the mean event length time was 2:33 minutes. We detected 8 moose during study year 1, 23 during year 2, and 29 during year 3. Monthly detections were in: July (n = 15), April (n = 10), June, August, and December (all n = 6), September (n = 5), with other months < 5. No detections occurred during February. We detected 18 adult females, 14 adult males, 5 cow-calf pairs, 4 calves, and sex and age were undetermined in 14 detections. Moose were primarily detected in rural parts of the city (n = 47), with most found along the eastern edge (Fig. 2). Ten detec- tions were at peri-urban sites, and 3 at urban sites. No detections occurred in the northern, industrial section. The dominant landcover class for most camera sites was built (n = 17), followed by agriculture (n = 8), greenspace (n = 2), aquatic (n = 1), and grassland (n = 1). No sites had forest and shrub as dominant landcover. Although trees and/or shrubs were present at all sites, only 4 sites con- tained > 7% total forest and shrub cover, and most sites contained < 2%. Fig. 2. Moose detections (n = 60) obtained from trail cameras (n = 29) deployed in Saskatoon, Saskatchewan, Canada, September 2020–September 2023. ALCES VOL. 60, 2024 URBAN TO RURAL PRAIRIE MOOSE 51 Statistical Model We analyzed moose detections at 28 camera sites, following exclusion of 2 cameras sites. Candidate model comparison and AIC results indicated no significant loss of explanatory power (P > 0.05) and a better fit (< AIC) after dropping month, dominant landcover class, green canopy cover, and vegetation height from the base model (Table 1). The best fit model had the lowest AIC score (223.9) and contained variables for time-of-day (x2 = 30.55, df = 3, P < 0.001) and development class (x2 = 10.38, df = 2, P = 0.0056). Moose were detected most fre- quently at night (z = 2.908, SE = 0.765, P = 0.0036; reference category “Afternoon,” Fig. 3), and they selected against urban sites (z = -2.694, SE = 0.678, P = 0.0071, refer- ence category “Rural”). A model containing the outlier site (with 57% of all moose detections) included the month and dominant landcover class vari- ables as significant predictor variables (Table S1). DISCUSSION Moose have been thought unable to thrive in the Canadian prairies due to the semi-arid climate, lack of forest cover, high road den- sity, extensive agriculture, and landscape composition (Laforge et al. 2016). However, moose are also highly mobile and capable of dispersing broadly (Arsenault 2000). Historically, conditions were not favorable for their establishment in agriculture-domi- nated areas. Fifty years ago, agricultural areas had large families that hunted inten- sively, but as rural populations have declined, hunting has waned dramatically. Although moose hunting is allowed around the City of Saskatoon, there is no hunting within city limits. Saskatoon may be functioning as a sanctuary for moose, with no hunting pres- sure and few, if any, medium to large preda- tors (Perry et al. 2020). Saskatoon is surrounded by farmland with high yearly Table 1. GLM candidate model comparison results for selecting the best fit model of moose occurrence (n = 28 cameras) in Saskatoon, Saskatchewan, Canada, 2020-2023. Number of model variables (K), evaluation of model fit (AIC), and AIC difference between best fit and other candidate models (ΔAIC). Lowest AIC score indicated best fit model. Model K AIC ΔAIC Time_of_Day + Development 2 223.9 0.0 Time_of_Day + Development + Avg_Veg_Height 3 224.2 0.3 Time_of_Day + Development + Avg_Veg_Height + Canopy_Cover 4 224.6 0.7 Time_of_Day + Development + Avg_Veg_Height + Canopy_Cover + Dom_Landcover 5 226.3 2.4 Month + Time_of_Day + Development + Avg_Veg_Height + Canopy_Cover + Dom_Landcover 6 232.7 8.8 Fig. 3. Mean and standard error (SE) of moose detections per time-of-day from trail cameras in Saskatoon, Saskatchewan, Canada, 2020- 2023. Asterix (*) indicates a significant statistical difference between variable levels (reference category “Afternoon”) based on best fit model results (P < 0.05). URBAN TO RURAL PRAIRIE MOOSE ALCES VOL. 60, 2024 52 seeding rates of high-value crop types including oilseeds such as canola and soy- beans, and pulses such as field peas and lentils among others (Government of Saskatchewan 2024a). The selection of agri- cultural lands by moose was found to increase with higher densities of oilseed and pulse crops, as well as with the density of cover crops and forest (Laforge et al. 2017b), and this is likely an important influence on moose presence in our study area. Historically, moose occasionally dispersed through these areas, but as they adapted to using agricultural crops with extremely high nutritional value, utilizing pothole wetlands for cooling in summer heat, and finding safety from hunters and primary predators, survival has likely increased (Laforge et al. 2016, Laforge et al. 2017b). We believe these factors have contributed importantly to the ongoing moose expansion into the prai- ries and their establishment near urban environments. Moose have also colonized other urban and peri-urban landscapes, including in Alaska (Welch et al. 2015), Utah (Wolfe et al. 2010), Massachusetts, New York, and Connecticut, among others (Wattles and DeStefano 2011). The high levels of primary productivity often found in suburban areas (e.g., gardens, greenways, and parks) may be an attractant for moose (Welch et al. 2015). Road salt is used in the winter to de-ice roads, and this may also be a contributing factor to increased moose presence near urban areas through the formation of road- side mineral licks created by spring runoff (Leblond et al. 2007, Rea et al. 2021). The presence of very young calves of the year and cow-calf pairs detected yearly in our study area both during and following the calving season, as well as the year-round occurrences of adults of both sexes, suggests to us an established population that likely uses a mix of the urban, peri-urban, and adjacent farmland habitats. The negative association we found within highly devel- oped, urban sites in our study area may reflect some degree of avoidance to areas with high human activity and density, or simply the lack of natural features or suitable travel corridors that are typical of many inner-city areas. However, although moose presence was greatest in the rural sites, our research findings and recent media stories confirm that moose occasionally move through almost any part of the city. The high moose detections found at the single loca- tion east of the city is notable. This site was surrounded by farmed cropland and adjacent to a tree-ringed wetland pothole. It is likely that some combination of these habitat fea- tures (such as cover, forage, and shelter) had created highly favorable conditions that either attracted or kept moose in this area. Diurnal detection patterns in our study area were as expected. Moose detections were least likely during daylight hours, with detections increasing in the evening and peaking at night. This is consistent with the known activity patterns of moose being most active at crepuscular and night times (Risenhoover 1986, Dussault et al. 2006). Consequently, moose-vehicle collisions tend to occur in the highest frequencies at dawn, dusk, and night (Dussault et al. 2006, Klassen and Rea 2008). Between 2000-2020, 52% of all fatal wildlife-vehicle collisions in Canada involved moose (Barrett et al. 2023). Increased moose presence in Saskatoon may also increase the risk potential for moose-ve- hicle collisions related to heightened expo- sure to high road density and traffic volume (Joyce and Mahoney 2001). Management Implications Moose living in peri-urban and urban envi- ronments present significant risks to human safety. Vehicle collisions can occur any time moose are associated with streets and ALCES VOL. 60, 2024 URBAN TO RURAL PRAIRIE MOOSE 53 highways (e.g., 5 Saskatchewan highway collisions involving moose occurred within 30 minutes in a single evening; CBC News 2024). Further risk considerations include the potential for private and public land property damage (e.g., gardens, fences, trees, shrubs, etc.), safety issues related to aggressive behaviour, and property damage and likelihood of injury or death from collisions with trains and other modes of transportation. Management preparation and response considerations should include increasing public awareness and education, installing fencing to keep moose out of areas that are of highest concern as well as one- way fencing that will allow moose to escape while preventing them from returning, improved signage that is larger and lighted to better warn drivers in high-risk areas, and continued support of wildlife immobiliza- tion and translocation programs that appear to be effective (Brook et al. 2018, unpub- lished data). Future research should consider the use of GPS collars and a broader network of trail cameras to better understand popula- tion and habitat use dynamics in the peri-ur- ban environment, and further investigate seasonal and temporal patterns with respect to annual variations in human activity, cam- era detectability, environmental conditions, and crop cover and growth stage. ACKNOWLEDGEMENTS Our study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Urban Wildlife Information Network (UWIN), the University of Saskatchewan, the College of Agriculture and Bioresources, the Department of Animal and Poultry Science, the Saskatoon Forestry Farm Park and Zoo, the Alexander Malcolm Shaw Memorial Graduate Scholarship, the Harris and Lauretta and Raymond Earl Parr Memorial Scholarship in Agriculture, the Putnam Family Memorial Postgraduate Scholarship, the L.H. Hantelman Postgraduate Scholarship, the Gabriel Dumont Scholarship Foundation, the Margaret Skeel Graduate Student Scholarship, and the John Baerg Scholarship. We thank our partners at Meewasin Valley Authority, Wild About Saskatoon, the Saskatoon Forestry Farm Park and Zoo, the City of Saskatoon, and the Saskatoon Parks Department. We also thank G. Hooey, C. Savage, R. Grilz, D. Clark, and EcoQuest for their individual contributions. REFERENCES Anderson, M., B. 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C. stoner. 2010. A history of moose management in Utah. Alces 46: 37-52. zhou, J., G. P. kofinAs, K. kieLLAnd, R. B. Boone, L. Prugh, and K. D. tAPe. 2022. Climate change, moose, and subsistence harvest: socio-ecological assessment of Nuiqsut, Alaska. Ecology and Society 37(3): 29. doi: 10.5751/ES-13175-270329. https://doi.org/10.1371/journal.pone.0152636. https://doi.org/10.3390/foods12040824. https://doi.org/10.1002/jwmg.21459 https://doi.org/10.1002/jwmg.21649 https://doi.org/10.5751/ES-13175-270329. ALCES VOL. 60, 2024 URBAN TO RURAL PRAIRIE MOOSE 57 SUPPLEMENTARY TABLE Table S1. GLM candidate model comparison results for selecting the best fit model of moose occurrence (n = 29 cameras) in Saskatoon, Saskatchewan, Canada, 2020-2023. Number of model variables (K), evaluation of model fit (AIC), and AIC difference between best fit and other candidate models (ΔAIC). Lowest AIC score indicated best fit model. Model K AIC ΔAIC Month + Time_of_Day + Development + Dom_Landcover 4 378.1 0.0 Month + Time_of_Day + Development + Canopy_Cover + Dom_Landcover 5 379.8 1.7 Month + Time_of_Day + Development + Avg_Veg_Height + Canopy_Cover + Dom_Landcover 6 381.8 3.7 _Hlk176850167 _Hlk188793024 _Hlk194657217 _Hlk188793133 _Hlk188793224 _Hlk188793606 _Hlk188803244 _Hlk188803178 _Hlk62464216 _Hlk188861905 _Hlk195171561 _Hlk188802871 _Hlk195172425 _Hlk26020696 _Hlk195172962 _Hlk195179263 _Hlk188802721 _Hlk194662880 _Hlk195174210 _Hlk195174220 _Hlk188796127 _Hlk195084206 _Hlk195084275 _Hlk187929077 _Hlk195174538 _Hlk195084813 _Hlk188692330 _Hlk188802520 _Hlk195084905 _Hlk195088862 _Hlk195176066 _Hlk195088830 _Hlk195088839 _Hlk188802543 _Hlk195088851 _Hlk195087054 _Hlk195087069 _Hlk188802129 _Hlk188691830 _Hlk188802186 _Hlk188802227 _Hlk188802241 _Hlk188802306 _Hlk188802332 _Hlk188800749 _Hlk188867989 _Hlk188802387 _Hlk188802445 _Hlk163034538 _Hlk194657593