Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Gauthier, Danielle
Master of Science - MSc
University of British Columbia
Wolves (Canis lupus) population management is a priority for conservationists, wildlife managers, hunters, trappers, cattle producers, and First Nations. Ideally, a survey technique should allow individual identification to increase managers’ ability to precisely and accurately estimate population trends in abundance, distribution, density and pack structure. However, wolf monitoring is notoriously difficult because of the species’ low density and elusive nature. Furthermore, lowering operating costs is a priority for wildlife agencies. In some cases, non-invasive survey techniques have lower monitoring costs relative to more invasive approaches like capture and tagging. There have been very promising developments in bioacoustics that could answer these questions. Morphological differences in the vocal tract of wolves mean that individuals have measurably distinct vocalizations. Minimum counts, age class, and individual identification have been achieved in previous studies, yet these methods remain to be proven in the field where little or no information about wolves or packs is available. The goal of this study was to assess bioacoustics for monitoring wolf occupancy by deploying autonomous recording units (ARU) in southeastern British Columbia. We hypothesized that deploying ARUs at wolf rendezvous sites would yield the best number and quality of recordings. To do so, we created a predictive map of rendezvous sites, and used it in an attempt to find rendezvous sites through grounds searches and elicited howling surveys. We paired ARUs with cameras traps to compare detection probability, overlap, and environmental covariates through single and multiple-method occupancy models. We also compared costs for equipment, deployment, and analysis. Finally, howling phenology was investigated to detect trends that could be used to refine recording schedules and reduce requirements for field data. ARUs proved more efficient at detecting wolves than camera traps paired at the same location, however the former method is costlier and time consuming in the field and the lab. No temporal trends were found in howling phenology to potentially refine recording schedules. Additional effort to target rendezvous sites mostly proved futile as only one site was found. Although bioacoustics is a promising method for wolf monitoring, further research is needed before becoming a practical technique for wildlife managers.
ASSESSING PASSIVE RECORDING OF HOWLS TO MEASURE WOLF (CANIS LUPUS) OCCUPANCY IN THE EAST KOOTENAYS, BRITISH COLUMBIA by Danielle Gauthier B.Sc., Université du Québec à Trois-Rivières, 2009 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2021 © Danielle Gauthier, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Assessing passive recording of howls to measure wolf (Canis lupus) occupancy in the East Kootenays, British Columbia. submitted by Danielle Gauthier in partial fulfillment of the requirements for the degree of Master of Science in Biology Examining Committee: Dr. Adam T. Ford, Biology Department, Irving K. Barber Faculty of Science Supervisor Dr. Karen Hodges, Biology Department, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Garth Mowat, Ministry of Forests, Lands, Natural Resource Operations & Rural Development Supervisory Committee Member Dr. Miles Thorogood, Faculty of Creative and Critical Studies Additional Examiner iii Abstract Wolves (Canis lupus) population management is a priority for conservationists, wildlife managers, hunters, trappers, cattle producers, and First Nations. Ideally, a survey technique should allow individual identification to increase managers’ ability to precisely and accurately estimate population trends in abundance, distribution, density and pack structure. However, wolf monitoring is notoriously difficult because of the species’ low density and elusive nature. Furthermore, lowering operating costs is a priority for wildlife agencies. In some cases, non-invasive survey techniques have lower monitoring costs relative to more invasive approaches like capture and tagging. There have been very promising developments in bioacoustics that could answer these questions. Morphological differences in the vocal tract of wolves mean that individuals have measurably distinct vocalizations. Minimum counts, age class, and individual identification have been achieved in previous studies, yet these methods remain to be proven in the field where little or no information about wolves or packs is available. The goal of this study was to assess bioacoustics for monitoring wolf occupancy by deploying autonomous recording units (ARU) in southeastern British Columbia. We hypothesized that deploying ARUs at wolf rendezvous sites would yield the best number and quality of recordings. To do so, we created a predictive map of rendezvous sites, and used it in an attempt to find rendezvous sites through grounds searches and elicited howling surveys. We paired ARUs with cameras traps to compare detection probability, overlap, and environmental covariates through single and multiple-method occupancy models. We also compared costs for equipment, deployment, and analysis. Finally, howling phenology was investigated to detect trends that could be used to refine recording schedules and reduce requirements for field data. iv ARUs proved more efficient at detecting wolves than camera traps paired at the same location, however the former method is costlier and time consuming in the field and the lab. No temporal trends were found in howling phenology to potentially refine recording schedules. Additional effort to target rendezvous sites mostly proved futile as only one site was found. Although bioacoustics is a promising method for wolf monitoring, further research is needed before becoming a practical technique for wildlife managers. v Lay Summary Although wolves are a charismatic species that generates considerable interest from the public, their elusiveness makes them difficult to survey. Wolf howls could be used as a way to detect their presence or even their individual identities. I deployed autonomous recording units at camera trap sites to compare wolf detection using both detection methods. I also attempted to predict and target rendezvous sites to deploy additional devices, but this proved unsuccessful in the field. Comparing image and sound data revealed that howls are a better method to detect wolf presence, even without prior knowledge of wolf location. Still early in its developmental stages, wolf bioacoustics could inform wildlife managers in a novel way to complement existing yet imperfect and costly field methods to count wolves and map their pack territories. vi Preface This research project was co-initiated by Tara Szkorupa from British Columbia’s Ministry of Forests, Lands and Natural Resource Operations to investigate ways of enhancing wolf monitoring in the Kootenay region along with their multi-species camera trap monitoring project already in place. The choice to focus on bioacoustics as a survey method was mine, but committee members have helped with the objectives, the study design, the selection of statistical analysis methods and draft reviews. General support has been obtained from colleagues in the WiRE lab at UBC Okanagan. Undergraduates students contributed to raw data processing of the sound files and all pictures classification from 2018 used in this project. Most analyses have been conducted by me but greatly facilitated by code snippets supplied by Dr. Adam Ford for data transformation and the entire generalized additive model. Further code for covariates extraction has been adapted from Emily Sunter’s scripts for her own thesis project. All scripts and bioacoustics dataset are available on OSF (https://osf.io/q6zxv/) and the entire thesis was written by me and improved with edits from my committee members. No ethics approval was required as all survey methods used were noninvasive. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................. v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... vii List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................ x Acknowledgements ...................................................................................................................... xii Chapter 1: General introduction ................................................................................................. 1 1.1 Wolf monitoring .............................................................................................................. 1 1.2 Current survey techniques ............................................................................................... 3 1.3 The emergence of bioacoustics for wolves ..................................................................... 7 1.4 Occupancy modelling .................................................................................................... 12 1.5 Thesis objectives and hypotheses .................................................................................. 17 Chapter 2: Comparing passive recording of howls to camera trap data to monitor wolf (Canis lupus) occupancy in the East Kootenays, British Columbia. ....................................... 19 2.1 Introduction ................................................................................................................... 19 2.2 Materials and methods ................................................................................................... 22 2.2.1 Study area .............................................................................................................. 22 2.2.2 Camera trap deployment and pictures classification ............................................. 23 2.2.3 Autonomous recording units deployment and spectrogram analyses ................... 24 2.2.4 Analyses ................................................................................................................ 25 2.3 Results ........................................................................................................................... 28 viii 2.3.1 Howling phenology ............................................................................................... 28 2.3.2 Occupancy models ................................................................................................. 30 2.3.3 Cost analysis .......................................................................................................... 38 2.4 Discussion ...................................................................................................................... 39 2.4.2 Howling phenology ............................................................................................... 40 2.4.4 Occupancy models ................................................................................................. 41 2.4.5 Cost analysis .......................................................................................................... 44 2.5 Conclusion ..................................................................................................................... 45 Chapter 3: General conclusion ................................................................................................... 46 3.1 Attempts to target rendezvous sites ............................................................................... 46 3.2 Synthesis and recommendations .................................................................................... 50 References .................................................................................................................................... 54 Appendix ...................................................................................................................................... 63 Appendix A: Building and using a predictive map for wolf rendezvous sites in the East Kootenays. ................................................................................................................................. 63 ix List of Tables Table 1: Occupancy and detection covariates used in occupancy models and their hypothesized relationships (positive, negative or not defined) on occupancy and detection method. ................ 26 Table 2: Model selection by Akaike information criterion corrected for small samples (AICc) for single method autonomous recording units (ARU) models, single method camera traps models and multi method models. Psi is occupancy at the cell level, theta is occupancy at the site level and p is the probability of detection. K represents the number of parameters, DAICc the difference in AICc value relative to the best model, w the Aikake weight, model L the model likelihood and -2Log(L) represents twice the negative log-likelihood value. ............................... 33 Table 3: Untransformed Estimates of coefficients for covariates from the best models. .............. 34 Table 4: Equipment and work costs for deployment of 26 pairs of autonomous recording units (ARU) and camera traps and the subsequent processing of data for the month of August 2018 in the Flathead valley, British Columbia, Canada. ............................................................................ 39 Table 5: Logistic regression parameters (SE) and odds ratios from the highest ranked model describing gray wolf rendezvous sites in Idaho, USA 1996-2006 (Ausband et al., 2010). ........... 64 Table 6: Data used to build the predictive map of rendezvous sites for the Flathead valley, British Columbia, Canada. ........................................................................................................................ 66 x List of Figures Figure 1: Study area, the Flathead valley. Wildlife management units (WMU) 4-01 and 4-02, East Kootenays, British Columbia, Canada. UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC, Statistics Canada, United State census bureau, Natural Earth. ....................................... 23 Figure 2: Howling events detected per site during August 2018 in the Flathead valley of British Columbia, Canada. Full moon on August 26th. Recordings began 30 minutes before sunset and ended 30 minutes after sunrise (grey area). Black lines indicate sunrise and sunset. ................... 29 Figure 3: Trends in wolf howling phenology (smoothing terms plots). ARUhr 1 corresponds to 20:00. Top left: GAMM with hour as a smoothing term across the night. Top right: GAMM with day as a smoothing term across the month. Bottom left: GAM with hour as a smoothing term across the night. Bottom right: GAM with day as a smoothing term across the month. ............... 30 Figure 4: Detection history of the 26 paired devices used in the occupancy models deployed during August 2018 in the Flathead valley, British Columbia, Canada. White areas indicate device was active and black area indicates inactive device. Top plot autonomous recording units (ARU), bottom plot camera traps. ................................................................................................. 32 Figure 5: Detection probability of intercept occupancy models with standard error bars, using two different survey methods and two different modelling techniques: autonomous recording units (ARU) in a multi method (MM) model, camera traps in a multi method model, ARU in a single method (SM) model and camera traps in a single method model. ...................................... 35 Figure 6: Cumulative detection probability (p*) of intercept occupancy models, using two different survey method and two different modelling techniques: autonomous recording units (ARU) in a multi method (MM) model, camera traps in a multi method model, ARU in a single method (SM) model and camera traps in a single method model. ................................................ 36 xi Figure 7: Map of occupancy from the multi method (MM) model (top), autonomous recording units (ARU) single method model (middle), and camera traps single method model (bottom) from data acquired during August 2018 in the Flathead valley, British Columbia, Canada. Conditional occupancy of 1 (dark blue) indicates that a wolf has been detected in the grid cell at least once. ...................................................................................................................................... 37 Figure 8: Maps of the top three classes of predicted rendezvous sites with a probability >70% for the Flathead valley, British Columbia, Canada. Top left shows roads and trails surveyed and visited meadows in black, green ellipses highlight areas where local biologists and hunters thought rendezvous sites might be found. Top right shows stations for elicited howling surveys at night in grey. Black point shows where a response from a wolf was detected. Bottom left shows fresh scats (circles) and tracks (x) found during ground searches. Bottom right shows the unique active rendezvous site found in blue. [UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC]. ........................................................................................................................................ 48 Figure 9: Total harvest for wolf prey species (elk and moose) in the East Kootenays from 1998 to 2018 (Province of British Columbia, 2020). ................................................................................. 50 Figure 10: Map of the top three classes of predicted rendezvous sites with a probability >70% for the Flathead valley, British Columbia, Canada. UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC. ....................................................................................................................... 67 xii Acknowledgements The various stages of this project took place on unceded First Nation territories. Field work was conducted on Ktunaxa territory in the East Kootenays, while the preparation and subsequent analysis took place on Syilx territory, and finally the redaction was done on Mi’kmaw territory. This project has been made possible through scholarships from the Fonds de recherche du Québec – Nature et technologies (FRQNT) and the University of British Columbia (UBC), two grants from the Habitat Conservation Trust Foundation (HCTF), and funding and field support from British Columbia’s Ministry of Forests, Lands, Natural Resource Operations & Rural Development and Dr. Adam T. Ford’s Wildlife Restoration Ecology (WiRE) lab. I would like to thank my lab mates and founding members of the WiRE lab, Emily Sunter, Sultana Majid, and Chloe Wright for all the laughs, the encouragements and the support, especially with coding, throughout this degree. I am very grateful to the volunteers and paid undergraduate students who assisted with processing the audio recordings: Andres Mancera Barreto, Breanna Raymond, Brooklyn Maher, Caleb Wale, Daniela Ruffo, Jenna Scherger, Juliana Mitchell, and Megan Walwyn. Megan Walwyn and Thea Miles have also processed the camera trap data. A very special thanks to Thea Miles, my field assistant, who has been fully committed to the project since the beginning, proactive and hardworking, and has accomplished every task above expectations. Living 24/7 in a remote cabin with almost no time off with you has been a breeze and it is a testament to your positive and pragmatic personality. Stewart Clow and Laura Smit from the BC government have been invaluable assets to launch field work with their expertise of the area and throughout the summer with logistics. xiii From the staff at UBC, I owe particular thanks to Barb Lucente, the senior departmental administrative assistant who made the entire administrative process a pleasant experience by her professionalism and personable attitude. Lastly but not the least, I thank my committee members, Dr. Karen E. Hodges, Dr. Garth Mowat and my supervisor Dr. Adam T. Ford for their advice throughout this graduate degree. 1 Chapter 1: General introduction 1.1 Wolf monitoring Wolves (Canis lupus) are a priority species for conservationists (Whittington et al., 2011) wildlife managers, hunters, trappers, cattle producers, and First Nations (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014; Courchamp et al., 2018) resulting in the need to monitor their populations. In 2011, wolf density for British Columbia, Canada, was estimated at 7-13 wolves/1,000 km2 based on ungulate biomass (Kuzyk and Hatter, 2014). The first recommendation of the Management Plan for the Grey Wolf in British Columbia, Canada (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014) is “to improve the accuracy and precision of provincial and regional wolf population assessments”. The need for better assessments is especially important if wolf population sizes are subject to interventions such as culls to enhance caribou (Rangifer tarandus) abundance (Hervieux et al., 2014; Serrouya et al., 2015). Therefore, managers need rigorous data to justify these measures, track their impact and reassess interventions on wolf populations (Bridger, 2019). Additional issues regarding wolf management in western Canada include conservation, game population management, and human-wildlife conflict (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). Wildlife monitoring often involves setting objectives for populations and repeatedly surveying those populations over time to ensure that trends are stable or meeting targets. Deviation from objectives requires management practices, such as harvest regulation changes or habitat protection measures (Sutherland, 2006). However, the prohibitive costs associated with evidence-based management of large mammals are considerable obstacles for agencies operating 2 on limited budgets (Artelle et al., 2018; Kunkel et al., 2005). Specifically, monitoring the abundance and distribution of wolves is notoriously difficult because wolves occur at low density and are elusive (Ausband et al., 2010; Galaverni et al., 2012; Kunkel et al., 2005; van Oort et al., 2008). As such, a cost-effective means of monitoring wolves in a rigorous manner is a high priority, yet striking the balance between cost and rigor has been a challenge for managers worldwide (Kunkel et al., 2005). Complete censuses provide the most detailed information, but are rarely realistic when dealing with wild populations, especially with carnivores (Sutherland, 2006). Capture-recapture methods are recognized as a robust method to acquire abundance estimates, yet these approaches usually require animals to be individually identifiable through natural markings or tagging (Linden et al., 2017; Rowcliffe et al., 2008). Historically these methods required capturing individuals to mark them with tags, rings, notches, or coloring (Sutherland, 2006). Non-invasive survey techniques are growing along with concerns for animal welfare from the public, ethical committees, funders and researchers themselves (Zemanova, 2020). Indeed, several methods involve taking samples (e.g. blood, tissues), mutilating animals (e.g. notches on turtle shells) or implanting trackers or tags on individuals. Careful thought should be given to individual pain and stress versus the insight that can be gained and applied to rigorous population management (Zemanova, 2020). Furthermore, non-invasive methods can be a means to lower costs since capturing and handling carnivores is difficult and expensive (Long et al., 2008). Indeed, statistical analyses and models allow tracking changes in species abundance, distribution, and trends. However, these models need to be validated and calibrated regularly since they are based at their core on abundance data, highlighting the importance of sound and reliable survey techniques (Serrouya et al., 2017). Therefore, although wildlife managers have to 3 balance costs and sampling effort to achieve the accuracy and precision desired to assess populations, it is often preferable to use rigorous quantitative methods that estimate absolute abundance instead of measures of relative abundance based on indices that are more susceptible to biases (Hopkins and Kennedy, 2004; Long et al., 2008; Stephens et al., 2015). The relationship between these indices and absolute abundance are not always well understood and can apply in limited scenarios linked to density, behavior, activity level, management practices, etc. (Stephens et al., 2015). The ability to survey wolves through individual identification over large areas would greatly increase managers’ ability to precisely and accurately estimate trends in population abundance, density and pack structure (Kunkel et al., 2005). Information on habitat use and movement through space and time can also be useful to mitigate human-wildlife conflict and depredation. Therefore, methods that provide a wealth of information about these population characteristics maximize survey effort (Lamb et al., 2019). 1.2 Current survey techniques There are a number of established approaches to wolf monitoring that vary in cost and strength of inference. Established methods for monitoring wolves include GPS/VHF telemetry, snow tracking, citizen science, camera traps, howling surveys, and DNA-based surveys (Kunkel et al., 2005). Radiotelemetry has been used for territory mapping and to determine pack and territory size but is very expensive as at least one individual in every pack needs to be collared (Kunkel et al., 2005). It usually takes several years to acquire the data needed to obtain an estimate, which means population estimates may be wrong if population numbers vary through time (Kunkel et 4 al., 2005). Furthermore, there is an issue with social acceptability, because it is an invasive procedure and maintaining sufficient collared animals is deemed financially unrealistic when the number of packs is growing and/or populations are subject to harvest (Ausband, 2016; Ausband et al., 2014; Brennan et al., 2013). Snow tracking of wolves has been widely used in Nordic countries either from the air or on the ground, but it requires considerable effort and is limited in several ways (Kunkel et al., 2005). Snow tracking requires fresh snowfall that has not yet turned icy and good access for ground-based tracking or canopy openness for an aerial survey; these conditions are often limiting. In British Columbia, aerial snow-tracking is recommended for the northern part of the province (Ministry of Environment, Lands and Parks and Resources Inventory Branch, 1998), where it has been used in predator-prey relationship studies (Bergerud and Elliott, 1998; Serrouya et al., 2016). Observer experience is also critical. Unless tracks are followed until the pack is found, these signs serve only as an index of relative abundance by estimating the numbers of individual tracks. Inference is limited to winter habitat only, and as wolves may shift habitats in summer, snow tracking does not provide information on habitat use or dispersion throughout the whole year. However, further data can be acquired during ground survey such as DNA-sampling of scats (Kunkel et al., 2005). Citizen science tools such as social surveys of hunters, trappers, and outdoor enthusiasts’ wolf sightings are affordable and offer an opportunity to involve, educate, and raise awareness with the public, but participants’ interest and motivation need to be maintained (Ausband et al., 2014; Kojola et al., 2014). Considering how wolf management can be a polarizing topic, it might be more advisable to assess populations with objective methods that are not influenced by personal emotions and opinions to maintain public confidence in management (Ausband et al., 2014; B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014; Kunkel et al., 5 2005). Citizen science sampling may have uneven coverage that could lead to biases and provide little more than general population trends even if done every year. Harvest data from management agencies can provide information on distribution, sex, and hunting pressure but needs to be controlled for confounding factors such as local hunting effort, reporting rate, and wolves’ individual likelihood of being captured. Further examination of those carcasses and individuals killed on roads or through control programs can inform on age and reproduction history. However, these lab analyses can be time consuming and expensive, and large samples are required to provide measures of trend and vital rates through statistical population reconstruction (Clawson et al., 2017; Kunkel et al., 2005; Skalski et al., 2012a, 2012b). DNA-based surveys yield information at the individual level, but are costly in terms of collection and processing depending on the precision desired (Stenglein et al., 2010). The costs for genetic analysis rise quickly with large sample size. DNA extraction from wolf scats and hair can be used to identify individuals and sex, but not age class (Thiessen, 2007). DNA surveys have often been done in winter as cold temperatures preserve DNA longer (Rutledge et al., 2009), but newer approaches have sampled scat at rendezvous sites in the summer (Ausband et al., 2010; Stenglein et al., 2010). Rendezvous sites are areas where juveniles, or pups, stay after emerging from the den at 4-6 weeks of age (mid-June). These sites are located near water and are generally open grassy areas to facilitate surveillance of pups by adults while other pack members go foraging. Pups can be moved between several rendezvous sites until they can travel with the rest of the pack towards the end of September (Ausband et al., 2010; Joslin, 1967; Mech, 1974). Targeting these locations instead of along trails increases the likelihood of getting scat from all pack members, 6 distinguishing individuals as adults or pups based on scat size, and reducing sampling coyote (Canis latrans) scats mistakenly (Ausband et al., 2010; Stenglein et al., 2010). As an increasingly popular method, camera traps can survey multiple species at once (Burton et al., 2015; Rowcliffe et al., 2008; Steenweg et al., 2017, 2016). This relatively inexpensive method ($200-1500 per camera) could provide wolf densities, distribution, and trends (O’Connell et al., 2011). Unless there are distinctive pelage markings to distinguish individuals (Gipson et al., 2006), this method cannot be used with traditional mark-recapture analytical methods. Therefore only relative abundance and density of unmarked populations can be gained from cameras at the moment (Palencia et al., 2021). Capture-recapture methods for species without individual identification have made significant progress recently. For example, some camera traps studies integrate distance sampling or investigate time to detection, but these methods need further testing of how they correlate to absolute abundance (Howe et al., 2017; Moeller, 2017; Palencia et al., 2021; Rowcliffe et al., 2008). Some of these models also require random designs to meet statistical assumptions (Howe et al., 2017), but considering the low density of wolves, it is often recommended to choose microsites such as travel corridors to maximize detection (Moskowitz and Huyett, 2019) . This deliberate selection of site goes beyond the simple stratified sampling design. Elicited howling surveys have been used for at least 50 years to detect wolf presence. These surveys broadcast a recording of wolves howling or the researchers imitate howling in an attempt to provoke a reply by one or multiple wolves (Kunkel et al., 2005). These types of surveys have high response rates when radio-collared packs and their homesites are targeted (Brennan et al., 2013; Harrington and Mech, 1982; Llaneza et al., 2005). Reply rates following a single person imitating howling or playback of a single wolf howling near known wolf locations 7 were 27% in Minnesota (Harrington and Mech, 1982), 39% in Poland (Nowak et al., 2007) and 13% in Italy (Gazzola et al., 2002). However, elicited howling without prior knowledge about pack location has much lower success; reply rates in Idaho were only 2% (Ausband et al., 2020). In those latter kind of studies, replies can be used as a general indicator that the area should be searched to locate a rendezvous sites (Ausband et al., 2010; Stenglein et al., 2010) and if howls can be heard at multiple locations within a night, triangulation can be achieved to refine ground searches direction and distance (Gable et al., 2018). At rendezvous sites, reproduction can be assessed because juvenile pups howl at higher frequencies than adults (Palacios et al., 2017). However, recent studies determined that aural estimation of wolf numbers during howling surveys are unreliable since humans cannot distinguish more than three concurrent sounds (Passilongo et al., 2015). As for aural estimations of reproduction, volunteers with various levels of experience successfully determined that pups were present 98% of the time on recordings with known numbers of wolves, but they also believed pups were present in 71% of recordings when no pups were howling (Palacios et al., 2017). 1.3 The emergence of bioacoustics for wolves Although howls have been used for many decades to detect wolves (Kunkel et al., 2005), technological advances are now allowing more insights than in situ estimation of population parameters (i.e. presence/non-detection, count estimates, presence of pups). The field of recording and analyzing sound produced by terrestrial or aquatic living organisms is known as bioacoustics (Browning et al., 2017). A review of terrestrial passive acoustic monitoring found 460 articles published between 1900 and 2018 with a notable increase between 2000 and 2010, 8 followed by exponential growth of publications (Sugai et al., 2019). The published papers covered several taxa that emit sounds such as invertebrates, bats, non-flying mammals, anurans, birds. Bioacoustics has also been widely used to study marine mammals, which are especially difficult to observe (Mellinger et al., 2007). Technological innovations have contributed to make more energy efficient, easy to deploy acoustic devices with large storage capacity that reduces fieldwork effort and costs, whereas computer algorithms are continuously making data processing faster and more efficient (Lahoz-Monfort and Magrath, 2021). Loading those algorithms onto field devices could further improve efficiency by identifying sounds in situ. An interesting application of this method with repurposed cell phones allows quick intervention when illegal activities are detected (e.g. chainsaws or gunshots) (Rainforest Connection, 2021). Acoustic data have been used to study occupancy, abundance, density and location, while soundscape ecology is looking at ecological processes, anthropogenic disturbance, community diversity and richness through indices calculated on the global recording such as acoustic complexity or dissimilarity (Browning et al., 2017; Sueur et al., 2014). To translate sound into data, bioacoustics recordings need to be classified to a taxonomic level or ultimately to an individual. Individual identification is essential to quantify several population vital rates and address important ecological questions about true abundance, density, demographic rates, movement, and behavioral interactions (Sutherland, 2006). In the field of bioacoustics, individual identification with an accuracy of at least 90% has been achieved for terrestrial mammals species such as barking fox (Alopex lagopus), swift fox (Vulpes velox), meerkat (Suricata suricatta), red deer (Cervus elaphus), Rock hyrax (Procavia capensis), Taurus ground squirrel (Spermophilus taurensis) and European ground squirrel (Spermophilus citellus) (Root-Gutteridge, 2013). 9 There have been very promising developments through the study of wolf howls, as morphological differences in the vocal tract of wolves produce individually-distinct vocalizations (Palacios et al., 2007; Passilongo et al., 2015; Tooze et al., 1990). Based on this knowledge, recorded replies from howling surveys can produce minimum counts during chorus howling at rendezvous sites, but absolute counts are not possible as some wolves might remain silent or be away from the pack (Passilongo et al., 2015).The method consists of visual inspections of the spectrogram and spectral envelope to distinguish individuals based on the fundamental frequency and amplitude of their howls. With this method, up to seven concurrent individuals have been identified out of a chorus of nine howling wolves from recordings with known numbers of wolves (Passilongo et al., 2015). It has proven difficult for researchers to identify more than 7 individuals howling simultaneously. In Italy, 92% of 29 chorus howls with a known number of wolves (real chorus size range: 2-9) were counted accurately with this technique (Passilongo et al., 2015). In addition to individual counts, determination of age structure may also be possible with bioacoustics (Palacios et al., 2016). Pups howl at a higher audio frequency (hertz) than adults during the first six to seven months of their lives, therefore reproductive status of packs can also be determined by analyzing the acoustic energy distribution of a chorus (Palacios et al., 2016). Using models to analyze ten acoustic variables and the acoustic energy distribution of chorus howls from captive and wild wolves in Spain, the presence of pups has been assessed with an accuracy of 94% (Palacios et al., 2016). Further quantitative approaches are emerging. Root-Gutteridge (2013) developed a computer algorithm in the software program MATLAB to extract wolves’ individual howls. Such computer algorithms combined with principal component analysis (PCA) can differentiate individuals through their howls with an accuracy of 97% from analyzing chorus howls and 100% 10 for individual howls. Additionally, artificial neural networks have further allowed attributing howls of unknown wolves to classify several subspecies (Root-Gutteridge, 2013). In that study, individual identification has been demonstrated by developing and running the algorithm on pre-recorded howls of wild and captive wolves (Root-Gutteridge, 2013). Since then, a similar technique has been used for Indian wolves (Canis lupus pallipes) with an accuracy of 98% (49 howls from 5 individuals) while training the model and 75% (15 out of 20 howls from 4 individuals) to identify new howls (Sadhukhan et al., 2021). In a small scale study of low quality recordings, howls of two grey wolves were identified with an accuracy of 100% using a discriminant function analysis (Hull et al., 2020). These recent developments in bioacoustics make these methods very promising as an efficient long term monitoring tool that should be further investigated. One of the key challenges in howl-based monitoring of wolves is ensuring that a recording device is active and close enough to an animal while it is actively howling. Eliciting howls is one approach to align recording and howling. However, this approach may raise concerns from people living nearby and affect wolf behaviour (Suter et al., 2017). Passive recording of howls avoids these challenges but raises additional hurdles, such as managing battery costs/life and timing recordings to maximize the probability of recording a howl. Understanding the diel pattern and phenology of wolf howling can help inform survey designs. In a study of spontaneous howling of radio collared wolves in Poland, peak howling hours were between 1800 and 0000 hours (Nowak et al., 2007). Howling was mostly done from the core of the territory and aimed at other pack members to determine their location (43% of cases), before leaving for foraging (22%), after reunions where the whole pack joined in howling (18%) or after successful hunting (5%). Only 2% of howls were identified as a form of inter pack communication, but not all packs’ territories in this study were adjacent to other territories. This 11 spacing could explain why there was so little inter-pack howling. The highest howling activity (58% of events) recorded in this European study was between July and October, and howling peaked in August when pups were kept at rendezvous sites (Nowak et al., 2007). Other studies have also studied and reported nightly peaks of howling: between 0400 and 0700 (Harrington and Mech, 1978), before 2300 and at dawn (after 0500) (Gazzola et al., 2002), between 2100 and 2300 and between 0400 and 0700 [Frommolt and Nikolsky, 1992 in (Gazzola et al., 2002)], and between 0400 and 0600 [Carbyn, 1975 in (Gazzola et al., 2002)]. Regarding seasonal peaks, investigation of elicited howling in Minnesota identified mid-summer to early autumn as the peak season (Harrington and Mech, 1982), with pups howling more frequently in July (Harrington and Mech, 1978). It has been hypothesized that increased howling in the summer might be linked to protection of pups and another peak occurs in winter (March) when wolves are looking for new mates (Harrington and Mech, 1982). Other studies also indicate that August has the highest rate of howling (Gazzola et al., 2002), but no second peak has been observed in Italy (Gazzola et al., 2002) or in Poland (Nowak et al., 2007). Various distances have been reported as the maximum distance from which wolf howls are clearly but imperfectibly recordable: 1 to 2 km in Poland (Nowak et al., 2007) and 3 km in a few other North American and European studies (Barber‐Meyer et al., 2020; Harrington and Mech, 1982; Kunkel et al., 2005; Suter et al., 2017). The analysis of sound files is affordable as informatics tools are widely available (Passilongo et al., 2015). Indeed, with emerging software development, the entire analysis process could be streamlined in the lab to lower labor costs (Passilongo et al., 2015). Audio files can be stored and reused for various analyses without deterioration and machine learning tools can automate the processing of files. 12 Wolf bioacoustics is therefore a promising emerging field and the rapid evolution of the technical component and big data management systems make this approach worthy of further investigation. However, as not all management agencies have prior knowledge of wolf location, bioacoustics has to be an efficient method at detecting wolves in such a scenario. 1.4 Occupancy modelling Occupancy models are used to study the occurrence of species across the landscape. Survey methods will never detect a species perfectly, given that it is present in the study area. This imperfect detection is due to factors such as visibility, observer experience, and variable animal behavior through space and time. Occupancy models were developed to address imperfect detection (MacKenzie et al., 2017). When detectability is not accounted for (naïve estimates), populations measures (e.g. distribution or abundance) are inevitably underestimated because of false negatives (Kellner and Swihart, 2014). Detection probability can be estimated by repeatedly surveying a site and recording presence or non-detection of the target species. The resulting detection history is used to estimate a detection rate or probability. The aim of occupancy modelling is to estimate the probability that a target species occupies an area or to assess the proportion of an area occupied by a species (MacKenzie et al., 2017). Assumptions for occupancy models are: a) no change in occupancy for the target species within a sampling season (e.g. colonization or extinction); b) probability of occupancy is equal across sampling units, or can be explained by covariates (e.g. a sampling unit that would have an unknown desirable resource for the target species, would violate this assumption); 13 c) probability of detection, given occupancy, is equal across sampling units, or can be explained by covariates (e.g. a device working sporadically with no way to diagnose when it failed would violate this assumption); d) detection is independent between sites and survey occasions at a given site (e.g. an individual howling and detected simultaneously at more than one site would violate this assumption); e) no false positives (e.g. misidentification of a coyote as a wolf) The basic study design consists of dividing the study area in sampling units that can represent natural entities (e.g. ponds, forest stands) or be artificially defined (e.g. grid of 10 km x 10 km cells) depending on study objectives. The occupancy probability within a sampling unit should be between 0.2 and 0.8 for occupancy models to perform well (MacKenzie et al., 2017). Sampling units are then surveyed repeatedly to create a binomial detection history. The model estimates the probability of occupancy (psi or Y) and the detection probability (p), both of which can be modulated as a function of covariates (beta parameters or b) (MacKenzie et al., 2017). Survey methods should be selected to provide a detection probability above 0.3 if occupancy modelling is to perform well (MacKenzie et al., 2017). Covariates, for both occupancy and detection, can be linked to sampling site characteristics (e.g. vegetation density) and survey aspects (e.g. method, effort, observer experience) can be used as detection covariates (MacKenzie et al., 2017). In a typical occupancy modelling analysis, several models are created with different combinations of covariates and ranked according to a model selection metric, usually the Akaike Information Criterion (AIC), where the smallest value indicates the best predictive model among other candidates (MacKenzie et al., 2017). Models within D2 AIC are deemed competitive and 14 comparable, whereas models within D4-7 AIC have substantially less support and models with more than D10 AIC no support at all (Burnham and Anderson, 2002). The information criterion can, and should, be corrected for small samples (AICc) when there are fewer than 40 observations per parameter (Burnham and Anderson, 2002). This correction is based on effective sample size, which is difficult to determine for occupancy models as the number of parameters can be different for occupancy and detection. As such, the most conservative way to determine the effective sample size correction is to use the number of units being sampled (MacKenzie et al., 2017). Selection of covariates should be done with care because when there are too many for the number of sampled units, the model tends to overfit on chance alone and will have little prediction power for unsampled sites (MacKenzie et al., 2017). Consequently, more complex models tend to rank higher through AIC comparison. Further assessment of model performance is done by looking at AIC weight, indicating the weight of evidence in favor of each model (MacKenzie et al., 2017). Once the best model among several candidates has been determined through AIC, its performance should be assessed to evaluate how well it fits the observed data (MacKenzie et al., 2017). The goodness of fit for occupancy models is difficult to assess as sample size tends to be low in ecology and tests have not been developed yet, other than for single-species, single-season models. The latter is based on a chi-square test comparing the observed and expected frequency (i.e. number of sites) for each detection history. As it assumes no missing observations, sampling units with identical patterns of missing values can be considered as a cohort on which chi-square tests comparison can be done (MacKenzie and Bailey, 2004). This method has limited applicability when there are several missing values or surveys occasions (more than 10), and a small number of sampling units. Such scenarios result in a multiplication of potential detection histories, making it difficult to meet the chi-square test assumption that all frequencies are at 15 least 5 (MacKenzie and Bailey, 2004). While goodness of fit tests are lacking for occupancy models, an alternative to assess models performance, is to compare their prediction to a different dataset (MacKenzie et al., 2017). The simplest occupancy model looks at single-species, single-season occupancy, but other models can assess local rates of colonization and extinction (single-species, multiple-season), co-occurrence or exclusion between species (multiple-species, single/multiple-season) or species states such as juvenile/adults, breeders/non-breeders (multiple-state, single/multiple-season) (MacKenzie et al., 2017). Another approach is a multi-scale model, which adds a third level of information to the estimates at the unit level and at the survey level (space and time replicates). This third level of information can come from multiple detection methods used in the same unit during the same period and this is why multi-scale models are often known as multi-methods (Nichols et al., 2008). This type of model assesses detection probability by methods, such as visual and aural sampling devices. Although detection from multiple survey methods could be pooled in a simpler model, multi-scale models yield more information on detection probability since one data stream can indicate that the species was available for detection by the other method (Nichols et al., 2008). Furthermore, multi-scale models account for unequal survey effort or missing data by having a distinct detection history for each method, therefore combining data from multiple methods is not recommended (Nichols et al., 2008). The purpose of occupancy modelling is to study distribution and geographic range, habitat relationships, turnover rate, and co-occurrence of species (MacKenzie et al., 2017). Occupancy modeling is not designed to answer questions about individuals (e.g. abundance, survival, movement). Nevertheless, occupancy has been used as a surrogate for abundance (MacKenzie et al., 2017). One way to do so is to set sampling unit size equal to the home-range or territory size of the studied species. Equating occupancy with abundance with this approach 16 could be sensible if units are natural landscape features (e.g. watershed) that reasonably restrict individuals to a location, and inferences can be made about abundance of individuals within that home-range or territory. The use of a grid on continuous habitat is considered ill-advised by MacKenzie (2017) as individuals move freely across the artificial boundaries of a grid creating a lack of independence that needs to be considered before making inferences about abundance. This form of spatial correlation (detection of the same individual in multiple cells) is not problematic for occupancy modelling since the parameter of interest is species occupancy (MacKenzie et al., 2017). Other methods have been designed and tested for estimating abundance. In the conterminous United States of America (USA), as the wolf population increased and the species was delisted as an endangered species, research budgets have been reduced and new low-cost alternatives to telemetry are now required to keep monitoring wolves to avoid relisting (Kunkel et al., 2005). One approach to make inferences about abundance is a broad scale patch occupancy using a grid of cells corresponding to the average territory size (Ausband and Bassing, 2016; Latham et al., 2014; Miller et al., 2013; Rich et al., 2013). This approach would see changes in occupancy as a change in pack numbers (colonization/extinction) but would not inform on changes in the number of pack members or territory size. Thus, these authors (Ausband and Bassing, 2016; Latham et al., 2014; Miller et al., 2013; Rich et al., 2013) have warned that although wolf occupancy models performed well, they require regular calibration by conducting more intensive surveys to validate assumptions about territory size and pack size. The development of these methods has been facilitated in the USA by data on pack size and territory size acquired through telemetry while the species was listed as endangered. Survey methods used in those wolf occupancy models include several thousand surveys of hunters and 17 landowners (Miller et al., 2013; Rich et al., 2013), camera traps and DNA sampling (Ausband and Bassing, 2016) and telemetry data (Ausband and Bassing, 2016). In addition, simulation and sensitivity analyses can be used to assess the influence of study design and species ecology on inferences (Linden et al., 2017). A recent study simulating when wolf occupancy can scale up to abundance based on snow tracking has revealed that good estimates can be obtained, but only under certain population size and density (Stauffer et al., 2021). Monitoring abundance through occupancy modelling is not a self-contained method and relies heavily on other sampling schemes, sometimes from neighboring jurisdictions (Bassing, 2017). Estimates of pack size and territory size can be highly changeable in a given area through the modification of territory range, pack members leaving and joining, mortality and variable breeding success. Additionally, packs can also be fragmented through harvest where wolves are hunted and trapped (Mech and Boitani, 2003). 1.5 Thesis objectives and hypotheses The goals of my thesis research are to: 1) improve the design of passive recording studies for wolves by quantifying the mid-summer phenology of howling; 2) compare the effectiveness of bioacoustics and camera trapping for detecting wolves in an occupancy model framework; 3) compare the logistical costs for both methods to shed light on the viability of bioacoustics as a cost-effective, rigorous approach to monitor wolf abundance. To meet this first goal, I will investigate howling phenology and wolf detectability to determine where, when, and for how long ARUs and camera traps need to be deployed to detect wolves. From these data, I will quantify howling per hour throughout the recording period. This 18 summary will be useful to refine recording schedules for future studies by targeting peak hours of howling and reducing the amount of data to be processed. The second goal is to assess the potential of bioacoustics for occupancy modelling by using data from ARUs and camera traps. I hypothesize that ARUs will detect more wolves than camera traps as ARUs can survey a radius of several kilometers, unlike camera traps that only detect animals a few meters in front of the lens (Suter et al., 2017; Swann et al., 2004). I will compare occupancy model coefficients and conditional occupancy maps from bioacoustics and camera trap based approaches to measure detection. Third, to assess the utility of bioacoustics surveys to wildlife managers, I will compare material and labor costs between bioacoustics and camera traps. Comparison will be made for this specific study area, as if no devices, either cameras or ARU, had been purchased or deployed at the beginning of the month-long study. 19 Chapter 2: Comparing passive recording of howls to camera trap data to monitor wolf (Canis lupus) occupancy in the East Kootenays, British Columbia. 2.1 Introduction Wolves (Canis lupus) are a priority species for conservationists (Whittington et al., 2011) wildlife managers, hunters, trappers, cattle producers, and First Nations (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014; Courchamp et al., 2018) as their population management is implicated in endangered species recovery (Serrouya et al., 2015), conservation, game population management, and human-wildlife conflict (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). However, monitoring the abundance, distribution and trends in wolf populations is notoriously difficult because of the species’ low density and elusive nature (Ausband et al., 2010; Galaverni et al., 2012; Kunkel et al., 2005; van Oort et al., 2008). The use of non-invasive survey techniques to address concerns for animal welfare is growing, but also as a means to lower monitoring costs relative to more invasive approaches like capture and tagging. Controlling costs is also a priority for wildlife agencies with limited budgets (Artelle et al., 2018; Kunkel et al., 2005). Ideally, a survey technique would allow individual identification to increase managers’ ability to precisely and accurately estimate population abundance, density, pack structure, and trends (Kunkel et al., 2005). There have been very promising developments in wolf bioacoustics that could help determine those population parameters since morphological differences in the vocal tract of 20 wolves can produce individually distinct vocalizations (Palacios et al., 2007; Passilongo et al., 2015; Tooze et al., 1990). Minimum counts of individuals (Passilongo et al., 2015), age structure (Palacios et al., 2016), and individual identification (Root-Gutteridge et al., 2014) have been achieved so far, yet these methods remain to be proven in a field setting where little or no information about wolves or packs is available. Occupancy models are used to estimate the probability that a target species occupies an area or, to assess the proportion of an area occupied by a species (MacKenzie et al., 2017). As survey methods never detect species perfectly, occupancy models have been developed to specifically address the issue of imperfect detection and reduce underestimation caused by false negatives (Kellner and Swihart, 2014). Detection probability can be estimated by repeatedly surveying a site. Occupancy models assess the probability of occupancy (Y) and the detection probability (p), as a function of covariates ( b) describing site characteristics (e.g. vegetation density) or survey aspects (e.g. method, effort, observer experience). Although detection from multiple survey methods could be pooled in a simpler model, multi-scale models yield more information on detection probability since one data stream can indicate that the species was available for detection by the other method (Nichols et al., 2008). The availability of the species to be detected is accounted for in multi-scale models by the parameter theta (q), indicating the probability of occupancy at the sample site, given the sampling unit is occupied (Nichols et al., 2008). Furthermore, multi-scale models account for unequal survey effort or missing data by having a distinct detection history for each method, therefore combining data from multiple methods is not recommended (Nichols et al., 2008). The most recent assessments of wolf populations for British Columbia, Canada dates from 2014 (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). Wolf status 21 according to the Conservation Data Center is “apparently secure” (S4 rank) and wolves are on the provincial Yellow list meaning the species is secure. The other two possible lists are Red for species and ecosystems that are at risk of being lost (extirpated, endangered or threatened) and Blue for species and ecosystems of special concern (Province of British Columbia, 2021). The species is ubiquitous in the province, except on the Haida Gwaii archipelago (Kuzyk and Hatter, 2014), and has recolonized areas in the south following years of intense human harvest of wolves until the late 1950’s (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). The most recent estimate of population size at the provincial scale is 8,500 wolves (range 5,300 – 11,600) based on ungulate biomass estimates and wolf densities from neighboring jurisdictions (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014; Kuzyk and Hatter, 2014). Observational and harvest data seem to indicate that the population is stable or growing but can be limited by prey availability, human-caused mortality, competition and disease (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). As wolves are not threatened in British Columbia, provincial management objectives are to maintain the population at a self-sustaining level where they can fulfill their role in the ecosystem while preserving other endangered species where relevant (B.C. Ministry of Forests, Lands and Natural Resource Operations, 2014). Wolf management in British Columbia should also consider cultural needs such as hunting and trapping, economic impacts linked to livestock depredation and First nations concerns. Other than data from harvest, there are no centralized databases for wolves in British Columbia. There are camera trapping projects led by Parks Canada (Steenweg et al., 2019), provincial agencies (this project) and others, as well as localized collaring efforts (Serrouya et al., 2012) but no standardized inventory method deployed at large scale. 22 The goals of this project are to: 1) Improve the design of passive recording studies by quantifying the mid-summer phenology of wolf howling; 2) compare the effectiveness of bioacoustics and camera trapping in a wolf occupancy model framework; 3) compare the logistical costs for both methods to shed light on the viability of bioacoustics as a cost-effective, rigorous approach to monitor wolf abundance. 2.2 Materials and methods 2.2.1 Study area The 2823 km2 study area (Figure 1) encompasses Wildlife Management Units (WMU) 4-01 and 4-02, commonly known as Flathead valley in the East Kootenays region in southeast British Columbia, Canada. This area was selected to allow comparison between methods through occupancy modeling because 31 camera traps have been deployed since 2016 for a multi species camera trap study piloted by the provincial government. 23 Figure 1: Study area, the Flathead valley. Wildlife management units (WMU) 4-01 and 4-02, East Kootenays, British Columbia, Canada. UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC, Statistics Canada, United State census bureau, Natural Earth. 2.2.2 Camera trap deployment and pictures classification Thirty-one Hyperfire camera traps (model PC600 and PC800) were deployed in the Flathead valley in 2016 following a protocol developed by Parks Canada (Steenweg et al., 2015). The devices are operational year-round and were programmed to take a series of five pictures when movement was detected at the highest sensitivity setting. A 10x10 km grid was overlaid 24 over the study area and 1 camera was installed per cell. Camera trap locations were selected to increase detection probability for multiple species by targeting landscape features such as pinch points, narrow valleys, creek forks, and site-specific features such as trees rubbed by grizzly bears (Ursus arctos horribilis), mineral licks and three types of trails: human trails (i.e. hiking or horse-riding trails), road bed (i.e. disused forestry roads) or wildlife trails. Cameras were tied to trees at breast height 2-5 m from the trail; vegetation was cleared to avoid triggering the motion sensor needlessly. Of those 31 devices, 29 were still deployed in the Flathead valley in 2018. Pictures were grouped into events where an animal or group of individuals of the same species appeared during a short period of time. Series of pictures taken by five minutes or more apart are considered separate events (Steenweg et al., 2015). Events were classified in Timelapse software (Greenberg and Godin, 2012) by noting species, sex, and age class when possible. 2.2.3 Autonomous recording units deployment and spectrogram analyses Twenty-nine Wildlife Acousticsä SM4 autonomous recording units (ARU) were deployed between June 15th and August 31st, 2018. They were located within 15 m of the camera traps already deployed in the area. Devices were powered by 4D NiMH batteries and programmed to record at a sample rate of 8,000 Hz from 30 minutes before sunset until 30 minutes after sunrise (Suter et al., 2017) using the sm4config-2.1.1B software from Wildlife Acoustics. One memory card of 128 GB was used per device. All recordings made from July 31st until the devices were recovered at the end of August 2018 were processed. This period was prioritized as previous work suggested it is the best period to record wolf howls (Gazzola et al., 2002; Harrington and Mech, 1982; Nowak et al., 2007) and time constraints prevented processing the entire dataset. Howls were located on the recordings by 25 visually scanning for 2-3 seconds each one-minute-long segments of spectrograms in Raven Lite 2.0.0 (Cornell Lab of Ornithology, 2016). Six volunteers and two paid undergraduate students assisted with data processing. Series of howls separated by a one-minute pause were considered separate howling events (Harrington and Mech, 1978). 2.2.4 Analyses Temporal trends were investigated to see if howling patterns could be detected throughout the night. Such patterns could inform on the periods where survey effort should be concentrated. Hours and dates of howling events were used as smoothing terms in a generalized additive model and site effects were considered. The analyses were done in R (2013, R Core team) with the package gamm4. Camera trap and ARU detection data collected during August 2018 were used separately in single season-single species occupancy models, as well as combined into a multi method-single species model to compare detection probability and wolf distribution. Temporal sampling units (i.e. visits or surveys) were defined as a period of 24 hours starting at noon until 11:59 am the following day to focus on wolves’ nightly activity. Analyses were done in PRESENCE 2.12.31 (Nichols et al., 2008). Three sites were omitted because the devices were not deployed in the proper cell (Akamina and Big Springs) and ended up covering other jurisdictions (Alberta and Montana) or because the camera trap was not functional during the study period (Desolation Pass). Landscape features used as detection covariates for ARUs included canopy cover, curvature and roughness averaged within a 2 km buffer around the device as these could impede acoustic detection by limiting how sound travels (Nowak et al., 2007). Elevation and distance to 26 highway were also included as it could inform on background noise from wind and traffic (Browning et al., 2017). Site features such as the presence or absence of a grizzly rub tree or a mineral lick and the type of trail (either human trail, wildlife trail or road bed) were also used as detection covariates. As camera traps were deployed for a multi-species monitoring project, these site features were targeted in an attempt to detect a variety of species, but not all of them might be optimal locations for wolf detection. Rub trees might affect detection positively or negatively as some of them were lured and the type of trail could affect detection positively to various degrees as wolves might use these travel corridors (Burton et al., 2012; Rich et al., 2013) (Table 1). Table 1: Occupancy and detection covariates used in occupancy models and their hypothesized relationships (positive, negative or not defined) on occupancy and detection method. 27 As an alternative for occupancy covariates, a predictive map of wolf rendezvous sites was used as a way to create a single covariate that would combine curvature, roughness and NDVI (Appendix A). The predictive map was created in ArcGIS 10.6 following Ausband et al. (2010) and deemed a relevant occupancy covariate as wolves should concentrate at those sites or at least travel and howl in those areas between foraging expeditions. To create this predictive map, spatial data were obtained from open-source websites (USGS, GeoGratis, DataBC). Layers included Landsat imagery to create the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to assess profile curvature and roughness. Criteria used to select Landsat-8 imagery were dates between June and August 2017 to reflect peak vegetation growth and with less than 10% cloud cover. The finest resolution available for the study area was 20 m for the Canadian DEM and 30 m for Landsat imagery and the American DEM. Additional layers such as lakes and rivers were obtained from DataBC and used as binary rasters to exclude certain features where wolf presence is unlikely despite habitat characteristics that fit the criteria for wet meadows. A buffer of 1 km was used around main road classes (highway, arterial, collector and local) for further exclusions (Jimenez, 1992; Matteson, 1992; Mech et al., 2003; Thiel et al., 1998). The average predictive map values for each 10x10 km grid cells used as sampling units for the camera trap study were used as the occupancy covariate. All covariates were rescaled in PRESENCE (divided by a constant) and checked to make sure they were not correlated (for values above 0.7 or below -0.7). Models with multiple covariates were tested during preliminary analyses, however these models did not converge. No clear rule of thumb exists for occupancy models, but it has been suggested on occupancy model forums (researchgate.net, phidot.org) that for each parameter in a model, 5-20 replicates (sites or visits) are required. Therefore, covariates were tested individually in the models because the sample size was too small to test parameters simultaneously. Akaike's Information Criterion 28 corrected (AICc) for small samples was used by setting the effective sample size in PRESENCE as the number of units surveyed (26) (MacKenzie et al., 2017). AICc was used to identify the best models; models within D2 AIC are deemed competitive and comparable, whereas models within D4-7 AIC have substantially less support and model with more than D10 AIC no support at all (Burnham and Anderson, 2002). Costs for both methods were compared assuming the same number of devices are deployed and recovered after one month. Specific costs for ARUs were available from the planning, fieldwork and analysis phases of this project, whereas cost for camera traps equipment were obtained from online sources and deployment costs were estimated from personal experience. Hourly salary was set at $20/hr. 2.3 Results 2.3.1 Howling phenology Twenty-nine ARU devices were paired initially with camera traps already deployed in the study area. During August 2018, 74 howling events (Figure 2), defined as a series of howls separated by a one-minute pause, were captured over 743 ARU-recording nights. 29 Figure 2: Howling events detected per site during August 2018 in the Flathead valley of British Columbia, Canada. Full moon on August 26th. Recordings began 30 minutes before sunset and ended 30 minutes after sunrise (grey area). Black lines indicate sunrise and sunset. Wolves howled throughout the night, with no clear hourly pattern. Similarly, wolves howled on most nights in August. Results from a generalized additive mixed model, using the device location as a mixed effect detected no significant howling trends through the night (p = 0.56) or throughout the month (p = 0.15) (Figure 3). Removal of the sites as a mixed effect did not improve hourly (p=0.26) or monthly howling trends (p=0.13) (Figure 3). 30 Figure 3: Trends in wolf howling phenology (smoothing terms plots). ARUhr 1 corresponds to 20:00. Top left: GAMM with hour as a smoothing term across the night. Top right: GAMM with day as a smoothing term across the month. Bottom left: GAM with hour as a smoothing term across the night. Bottom right: GAM with day as a smoothing term across the month. 2.3.2 Occupancy models During August 2018, the ARUs detected wolf presence during 24 surveys (or visits) while operating for 662 recording nights. During the same period, camera traps detected wolf 31 presence 12 times while active for 729 camera trapping nights (Figure 4). At 1 wolf detection per 27.6 survey-nights, bioacoustics had about double the detections than camera traps at 1 detection per 60.8 survey-nights. The best model for the bioacoustics data used on their own indicated that given wolf presence, canopy cover negatively affects howl detection (Table 2 and Table 3). The top model for camera traps used on their own suggests that camera deployment near secondary forestry roads positively affected wolf detection (Table 2 and Table 3). In the multi-method occupancy model, parameters were further limited due to the addition of theta (q) as a parameter to model occupancy at the site level, given occupancy at the unit level. The predictive map for wolf rendezvous sites positively influenced occupancy probability and the survey methods are relevant detection covariates (Table 2 and Table 3). Irrespective of covariates, comparing intercept models for all methods reveals that although detection probability (given presence) is similar, combining both methods in a multi method occupancy model increases this detection probability from 0.07 (0.04 – 0.11 95% CI) to 0.19 (0.05 – 0.53 95% CI) for ARUs and from 0.07 (0.04 – 0.13 95% CI) to 0.08 (0.02 – 0.28 95% CI) for cameras (Figure 5 and Figure 6). Furthermore, in a model that combines both methods, detection by ARUs is higher than it is for cameras alone, because wolves were detected in more grid cells (Figure 7), not because howls were detected during more surveys at any given sites. 32 Figure 4: Detection history of the 26 paired devices used in the occupancy models deployed during August 2018 in the Flathead valley, British Columbia, Canada. White areas indicate device was active and black area indicates inactive device. Top plot autonomous recording units (ARU), bottom plot camera traps. 33 Table 2: Model selection by Akaike information criterion corrected for small samples (AICc) for single method autonomous recording units (ARU) models, single method camera traps models and multi method models. Psi is occupancy at the cell level, theta is occupancy at the site level and p is the probability of detection. K represents the number of parameters, DAICc the difference in AICc value relative to the best model, w the Aikake weight, model L the model likelihood and -2Log(L) represents twice the negative log-likelihood value. Single method – ARU Single method – Camera traps 34 Multi-method – ARU and camera traps Table 3: Untransformed Estimates of coefficients for covariates from the best models. 35 Figure 5: Detection probability of intercept occupancy models with standard error bars, using two different survey methods and two different modelling techniques: autonomous recording units (ARU) in a multi method (MM) model, camera traps in a multi method model, ARU in a single method (SM) model and camera traps in a single method model. 36 Figure 6: Cumulative detection probability (p*) of intercept occupancy models, using two different survey method and two different modelling techniques: autonomous recording units (ARU) in a multi method (MM) model, camera traps in a multi method model, ARU in a single method (SM) model and camera traps in a single method model. 0.000.100.200.300.400.500.600.700.800.901.000 100 200 300Cumulative detection probabilityVisitsSM ARUSM cameraMM ARUMM camera 37 Figure 7: Map of occupancy from the multi method (MM) model (top), autonomous recording units (ARU) single method model (middle), and camera traps single method model (bottom) from data acquired during August 2018 in the Flathead valley, British Columbia, Canada. Conditional occupancy of 1 (dark blue) indicates that a wolf has been detected in the grid cell at least once. 38 2.3.3 Cost analysis Bioacoustics is a much more expensive method than cameras (Table 4). The retail price is higher for ARUs than cameras (approximately $1,000 vs $600 CAD) and ARUs require memory cards with more storage capacity than do camera traps (128 vs 32 GB). Battery types are different (4D NIMh for ARU and 12AA NIMh for camera traps) but costs per device are similar (approximately $75) considering a charger and a few extra batteries are necessary for ARU servicing. Camera traps take longer to install as their orientation needs to be adjusted and vegetation needs to be cleared, but ARUs drain batteries much faster as they are recording continuously throughout the night and need servicing after about 2 weeks, incurring additional fuel expenses and labour costs. It took two people 52 hours to service all 29 ARUs originally deployed in the study area, sometime working in pairs for safety reasons, sometimes alone to increase efficiency. These costs would increase for a longer study duration. Finally, data processing by visually inspecting spectrograms is extremely time-consuming and makes labor costs considerably higher compared to camera data. Camera processing time is quicker because typically cameras collect data only when triggered. Yet, when costs are compared on the basis of number of detections overall, costs are surprisingly similar. 39 Table 4: Equipment and work costs for deployment of 26 pairs of autonomous recording units (ARU) and camera traps and the subsequent processing of data for the month of August 2018 in the Flathead valley, British Columbia, Canada. 2.4 Discussion Investigation of howling phenology did not reveal any trends in howling at night. ARUs performed better at determining occupancy by detecting wolves in more grid cells but did not get more repeated detections at any given site. Comparison of costs for an identical study design showed that equipment and labor associated with bioacoustics were more expensive, but considering their better performance at detecting wolves, cost are similar on a per detection basis. 40 2.4.2 Howling phenology Results from this study revealed no trends by analyzing 74 wolf howling events during August in a generalized additive model. Howling did not peak at specific times during the night or across the month. Previous studies have indicated that wolf howling can peak at various times: between 1800 and 0000 hours (Nowak et al., 2007), between 0400 and 0700 (Harrington and Mech, 1978), before 2300 and at dawn (after 0500) (Gazzola et al., 2002), between 2100 and 2300 and between 0400 and 0700 [Frommolt and Nikolsky, 1992 in (Gazzola et al., 2002)], and between 0400 and 0600 [Carbyn, 1975 in (Gazzola et al., 2002)]. However, most of these studies looked at elicited howling surveys near known wolf locations. It is possible that spontaneous howling events are much more random and may also occur frequently during daytime (McIntyre et al., 2017). Howling behaviour could be linked to internal or external stimuli that are not correlated with time (e.g. if wolves howl to coordinate with pack members before foraging, this behavior might not occur within a regular time after sunset) (Nowak et al., 2007). These results suggest that little could be gained by attempting to reduce costs and maximize detections by focusing on specific time periods at night in August. However, recordings collected during other bioacoustics survey with various recording schedules (e.g. anurans, songbirds) and deployed at random locations could provide valuable wolf presence information with little additional cost. 41 2.4.4 Occupancy models The five assumptions of occupancy have been reasonably met. No change in occupancy should occur in August as wolf dispersal takes place in winter and spring. The probability of occupancy should be equal across sampling units as all of them are mainly composed of forested area. Furthermore, the rendezvous site predictive map that has been used as an occupancy covariate accounts for human presence by excluding major roads and differences in topography and vegetation. The probability of detection could vary greatly among sites and between sampling methods, but covariates have been used to modulate detection probability accordingly. The assumption that detection is independent between survey occasion is met for howling because recording stops during daytime. As for camera traps, this is likely met unless a wolf is in the vicinity of a camera around noon and triggers the devices repeatedly. The assumption that detection is independent between sites is likely met considering the small area surveyed by camera traps and the distance between ARUs (~10 km) versus their recording radius of 2-3 km. No false detections should have been recorded since wolves’ howls are quite distinctive. All the howling events identified by volunteers have been reviewed and deemed unlikely to be from coyotes or domestic dogs. Inactive ARUs are explained by normal battery drain or the inability to service devices in active wildfire zones, whereas inactive camera traps are due to normal battery drain and being knocked out of position by wildlife (Figure 4). Bioacoustics detected wolves at more sites that camera traps. This result was foreseeable as ARUs survey a much larger radius than camera traps, combined with the fact that wolves howl at a low acoustic frequency (hertz) that travels far in space. Occupancy estimates based on bioacoustics data should be more representative of true occupancy for the 10 x 10 km2 grid cell than camera traps as ARUs sample a larger proportion of the area. However, detection 42 probability between methods is similar since howling behavior was not detected often at any given site. Considering how difficult it is to survey wolves, bioacoustics has promise for wolf detection surveys and should be better explored. The benefit of using multiple survey methods yet modelling their separate detection history in a multi-method model was highlighted by the detection probability of ARUs increasing from 0.07 when the method is used on its own to 0.19 in the multi-method model. Neither method is perfect as both produced false absence: in three cells wolves were detected by cameras only compared to eight cells where only ARUs detected wolves. Overlap in detection occurred in only three cells. As such, camera traps indicate more false absences. This comparison of camera traps and bioacoustics in occupancy models suggests that bioacoustics are indeed a better detection method than camera traps, yet low repetition of howling on several nights for one location keeps detection probability low when ARUs are used alone. The spatial arrangement of cameras might not have been optimal for wolf monitoring as the camera traps were deployed as part of a multi species study, therefore a variety of locations were chosen to capture more biodiversity, hence some are aimed at grizzly rub trees and mineral licks. The strongest model for cameras indicates with a weight of 0.98 that those deployed along roadbeds captured more wolf pictures as wolves travelled along those corridors. It should be specified that those roads were secondary forestry roads with very little traffic, not paved or maintained forestry roads. Ideally, sites should be chosen randomly to increase the predictive ability of any models and allow robust statistical analyses yet detecting elusive carnivores with camera traps is difficult without targeting travel corridors (Hofmeester et al., 2021). As for ARUs, the best model with a weight of 0.76, included canopy cover as a detection covariate. In this case, the model indicates that detection probability is better with lower canopy cover, which makes sense because sound travels better when vegetation is open (Browning et al., 2017). 43 One way to increase detections is to increase survey duration by leaving devices in the field longer. According to this dataset, ARUs would need to be deployed in a multi-method design for 258 days to achieve a cumulative detection probability of 80% when visits are defined on a nightly basis (Figure 6). The 80% threshold is impossible to achieve within a year with camera traps or ARUs in a single method model. Longer deployment would not be incurring much more costs for camera traps but would drastically increase fieldwork to service ARUs and lab work to process recordings. As the cameras traps are active all year long, it is possible to consult a larger database. This extended camera trap dataset from June to November 2018, shows that out of the 8 cells where only the ARUs detected wolf presence, with time, cameras would have detected wolf presence in 4 of those (McDougal on June 15th, McLatchie on June 12th, CabinPass on July 3rd, West Packhorse on June 15th and July 20th). Still, camera traps would not have detected wolf presence in the other four cells where ARU did detect them (Lower Desolation, Steve’s Camp, Morrissey, Storm). Longer deployments have limited benefits for survey methods that have low detection probability and it is generally recommended to increase the number of sites instead of the number of visits (MacKenzie et al., 2017). Although this study targeted August as literature indicated it is one of the best month for howling (Harrington and Mech, 1982), howling frequency might increase in the fall as wolves start travelling more, need to advertise their territory and coordinate better with pack mates as rendezvous sites change more often with increased pup mobility (Harrington and Mech, 1978). However, another study contradicts this increase in howling rate (Nowak et al., 2007). Indeed, spontaneous howling in Poland decreased significantly in the fall, winter and subsequent spring. Another way to increase detection probability is to change how a visit is defined. Instead of looking at detections on a nightly basis, data could be pooled, and weeks could be defined as a 44 visit. However, when data is pooled in such way, effort should be accounted for as a survey covariate since devices can fail to work continuously during extended periods. A similar project conducted in Alberta, Canada looked at ARUs paired with camera traps for a multi-species monitoring program to detect free ranging wolves (Garland et al., 2020). This study covered a much larger scale over two years but focused on a period from March to June and recorded for only 38 minutes per day (two time slots of 13 minutes and 2 time slots of 6 minutes). Garland et al (2020) also found similar detection probabilities between ARUs and camera traps. By changing visits length from daily to weekly to monthly intervals, detection probabilities for ARUs changed from 0.03 to 0.11 and 0.30 respectively (Garland et al., 2020). 2.4.5 Cost analysis The cost analysis revealed that bioacoustics is more expensive yet more efficient at detecting wolves. Servicing of ARUs was inevitable with this study design as devices started to fail after 2 weeks. Shorter deployment or different recording schedules could improve performance but would reduce detection probability, especially considering wolves are territorial and need time to circle around their territory to be available for detection (Hines et al., 2010). Servicing costs could also be reduced by using solar panels but would complicate initial deployment with more equipment to carry in remote places and increased costs per device. A better option would be to use more energy efficient devices such as AudioMoths. A study using those devices revealed that they needed servicing after 2 weeks just like devices used in this study, yet they recorded all day long (Barber‐Meyer et al., 2020). The main issue to make bioacoustics appealing to wildlife management agencies is to reduce the workload incurred by manually processing recordings. In the future, machine learning algorithms applied to both 45 images and acoustic signatures will likely lower the costs of using either method (Marchal et al., 2021). 2.5 Conclusion This study has demonstrated that bioacoustics are a promising survey method for wolves. It is possible to deploy ARUs without previous knowledge about wolf occurrence and collect presence/non-detection data to be used in an occupancy model aiming to monitor distribution and trends in occupancy. Spontaneous howling frequency and the detection radius of ARUs allowed better performance than camera traps detecting movement in a very limited area. Paired comparisons show that with a similar study design, ARUs and camera traps both yielded false absences, but ARUs did perform better at detecting wolves. ARUs could also be used in addition to camera traps, deployed at different locations informed by the predictive map of rendezvous sites. To make bioacoustics worth implementing several hurdles must be tackled through further research. Deployment in the field is quick, but servicing has to be reduced by using more energy efficient and less memory hungry devices or developing ways to trigger recording only when a certain sound pressure is detected, similar to bat recorders (Browning et al., 2017). This option would greatly help with post-processing by reducing the amount of data generated by continuously recording. Other ways to improve lab costs are research into computer algorithms to locate howls faster in recordings or at least eliminate recording sections with background noise. These major field and lab limitations must be solved to make wolf bioacoustics interesting to wildlife management agencies, therefore considerable fundamental research must still be conducted. 46 Chapter 3: General conclusion 3.1 Attempts to target rendezvous sites One of the key challenges in howl-based monitoring of wolves is recording animals while they are howling – which involves targeting times of the year and locations where recording devices will have the highest probability of successful ‘capture’. It has been posited that rendezvous sites are ideal areas to target for passive bioacoustics recordings because wolf howls are captured over a longer distance in the habitat types associated with these sites (e.g. absence of loud running water and dense cover) (Ausband et al., 2010). Reproduction could also be assessed by recording pups, and minimum counts by recording choruses. Understanding where on the landscape such sites are likely to occur is an important first step in designing a cost-effective monitoring program. A predictive rendezvous site model has been developed in Idaho and applied elsewhere with success (Ausband et al., 2010). When this project was originally developed, I built such a predictive map of rendezvous sites using ArcGIS to target sites where ARUs could be deployed between mid-June and September 2018 (see Appendix A). Indeed, a subset of 10 devices was meant to be deployed at active rendezvous sites identified through ground searches (Ausband et al., 2010). The predictive map indicated that 24% (667 km2) of habitat patches in the Flathead valley had a probability of more than 70% of having the roughness, curvature and NDVI characteristics corresponding with a rendezvous site (Ausband et al., 2010). Most of high-probability of use areas were concentrated around the dense river network in the south of the study area. The predictive map was shown to biologists and hunters (Garth Mowat, Stuart Clow, David Beranek) working in the region, as a means of qualitative validation. Also, coordinates 47 from previously known rendezvous sites were acquired (Bruce McLellan). Areas highlighted as having high potential for rendezvous sites were west of Silver Spring lake (westernmost ellipse) and then from north to south; the Barnes’ meadows, Flathead townsite, Harvey creek campground, the surroundings of Commerce road and 12% road junction, Elder creek road and Lower Nettie road (Figure 8, top left). All drivable roads were travelled at 20 km/h (Ausband et al., 2010) to look for fresh scats and tracks. Several signs were found around the Barnes meadows and on Cabin road (Figure 8, bottom left). In addition to elicited howling during daytime ground searches, nighttime elicited howling surveys were done at 99 stations. Three sets of 5 howls were broadcasted using a howler. Most routes were surveyed for 3 consecutive nights unless access was limited, or weather was not favorable. Only one reply was heard near lower Wigwam road (Figure 8, top right). Based on these signs of presence, the predictive map and satellite imagery from 2016, 241 locations were specifically targeted for ground searches (Figure 8, top left)., resulting in the discovery of only one active rendezvous site (Figure 8, bottom right). 48 Figure 8: Maps of the top three classes of predicted rendezvous sites with a probability >70% for the Flathead valley, British Columbia, Canada. Top left shows roads and trails surveyed and visited meadows in black, green ellipses highlight areas where local biologists and hunters thought rendezvous sites might be found. Top right shows stations for elicited howling surveys at night in grey. Black point shows where a response from a wolf was detected. Bottom left shows fresh scats (circles) and tracks (x) found during ground searches. Bottom right shows the unique active rendezvous site found in blue. [UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC]. The lack of success in finding wolves signs along the road could be explained by a lack of tracking experience. While scats were easy to find along roads and were seen almost all over the study area, fresh ones that might indicate recent presence were rare. Tracks were numerous in muddy areas around the Barnes meadows and between Cabin Pass and Storm but tracking them 49 over long distances proved difficult. One set of tracks was followed over 3.6 km but was lost once the individual veered off the road. Local hunting guides have mentioned how difficult it is to track wolves. Howling surveys had a near 0 success rate. Reply rates of 2% were reported in Alberta and Idaho from elicited howling survey mostly at dusk and dawn during rendezvous site searches (Ausband et al., 2020). Furthermore, the predictive map proved impractical as it was not precise enough. Even after discarding obviously irrelevant spots such as mountain ridges, vast expanses of land had high canopy cover or turned out to be dense riparian habitat. To make up for this lack of precision, further discrimination has been done once in the field with aerial imagery. Thus, 241 locations were visited, but only one active rendezvous site was found. Although efforts on ground searches were spread across the entire study area, efforts could have been expanded with more field personnel and increased access to off-road vehicles. Another hypothesis as to why only one rendezvous site was found is that howling was rarer in 2018 compared to the five years previous and wolf reproduction was low, ergo the use of rendezvous sites was low. A recent analysis of wolf and ungulate harvest suggests that wolf abundance is limited by prey availability and the species had reached carrying capacity in the Kootenays (region 4) by 2010 (Mowat et al., 2021). As hunting seasons for elk (Cervus canadensis) and moose (Alces alces) did not change during the 10 years preceding this study, the trend in harvest data is likely similar to abundance. Such harvest data suggests a decline in the ungulate population in the two years leading up to this study (Figure 9), which could explain low fecundity and pup survival (Kuzyk et al., 2018). 50 Figure 9: Total harvest for wolf prey species (elk and moose) in the East Kootenays from 1998 to 2018 (Province of British Columbia, 2020). 3.2 Synthesis and recommendations This study demonstrated that it is possible to use passive acoustic recording to monitor wolves without prior knowledge about wolf location. ARUs proved more successful in occupancy modelling at detecting wolf presence than the camera traps deployed at the same location. However, bioacoustics equipment is more costly, more fieldwork is required to service devices and most importantly, lab analyses are extremely time consuming. One way to make passive bioacoustics more efficient, could be to target time slots where wolves are more likely to howl to increase detection and minimize energy and memory consumption by devices. No trends 050010001500200025001998 2003 2008 2013 2018Total harvestYear 51 have been identified in the dataset that could inform on how to improve temporal deployment to increase detection. On the contrary, the results suggest that recording at any time during the night can yield wolf detections, indicating that using data from any other bioacoustics sampling design can be useful. As for spatial deployment, ARUs should be deployed at locations where ambient noise is minimal, habitat structure (e.g. vegetation, topography) will not impede sound travel and ideally where abiotic factors can be measured as detection covariates (e.g. temperature, wind strength and direction) (Browning et al., 2017). It proved extremely difficult to locate rendezvous sites with a team of two inexperienced wolf biologists, yet the results from this project indicate that the predictive map could be used simply as an aid when selecting ARU deployment sites, without extensive ground searches. Assuming breeding was low during the study period, it is possible that howling was also low if less intra-pack communication was required to locate pack members at rendezvous sites. However, howling studies often draw contradicting conclusion depending on pack density, pack size and methodology (McIntyre et al., 2017). It would be interesting in further studies to assess if pups can be recorded at random location to assess reproduction. As wolf populations fluctuate based on changes in prey numbers and years of little or no breeding may not be uncommon, this study indicates that targeting rendezvous sites has limitations that should be considered when using this approach. Although this study has used an occupancy model framework and studies in the USA have demonstrated that occupancy models can scale up to abundance (Ausband and Bassing, 2016; Latham et al., 2014; Miller et al., 2013; Rich et al., 2013), the method is useful only in limited scenarios to assess abundance. Indeed, this approach only performs well when territory size and pack size are accurately estimated, the sampling grid is defined accordingly to territory size, density is high and there are few gaps between packs (Stauffer et al., 2021). Therefore, wildlife management agencies should make a careful assessment of currently available 52 knowledge to answer those assumptions and evaluate the necessity and costs of deploying additional effort to gain this information essential to model calibration. Thus, occupancy models will never be a stand-alone method to monitor wolf abundance because these assumptions need to be validated with different survey methods. Managers might decide that it is more realistic considering budgets and human resources to monitor species based on distribution trends assessed by occupancy modelling, even if such management practices lead to more questions about why those trends are observed (MacKenzie et al., 2017). In the short term, monitoring distribution might be adequate while better survey methods and statistical models are developed. Camera traps are already deployed in several areas of British Columbia, and this method can provide the minimum detection probability of 0.3 required for occupancy models to perform well by defining an appropriate time interval for visits (e.g. weeks, months). Additional ARUs could be deployed in areas where camera traps are not specifically targeting travel corridors to increase wolf detection and decrease false absence. Partnerships with other researchers studying songbirds and anurans could be beneficial to offset costs. In the long term, one avenue worthy of further research is bioacoustics. Several hurdles must be tackled before it can truly become an interesting tool for wolf management and unleash its full potential. From a technical standpoint, more energy efficient devices such as AudioMoths could be used, or solar panels could be linked to ARUs, but would complicates deployment logistics in remote areas. Ultimately, if recordings could be triggered only when certain sound frequency are detected, both energy consumption and memory capacity could be reduced (Suter et al., 2017). Another way to improve processing could be to develop recognizers and classifiers to locate wolf howls in recordings automatically. 53 Bioacoustics might be able to answer some crucial questions for wolf monitoring. As howls can provide individual identification (Root-Gutteridge, 2013) and triangulation to locate the point where howls originated is possible (Blumstein et al., 2011; Papin et al., 2018; Rhinehart et al., 2020), bioacoustics could be used in territory mapping and to assess pack size through mark-recapture methods, minimum counts or estimates based on acoustic indices (Papin et al., 2019). Better understanding of howling behavior and howl characteristics is also needed to understand bioacoustics limitations. Further studies should investigate distances at which howls can be recorded under various environmental conditions (Barber‐Meyer et al., 2020) and how long does a howling survey needs to last to detect all pack members. Disparities between studies of howling behavior make it difficult to draw conclusions (e.g. harvested/unharvested wolf population, human density, pack and territory size, recolonizing/established populations, elicited or spontaneous howling). Future research should be undertaken in areas where wolves are already collared to validate methods. Biologging, which involves equipping individuals with recording devices (Couchoux et al., 2015; Lahoz-Monfort and Magrath, 2021; Lynch et al., 2013) could also be useful to study howling frequency inter- and intra-pack and how sound travels in the environment. When this project was first developed, very few studies looked at spontaneous howling in free ranging wolf populations (Nowak et al., 2007; Suter et al., 2017). Despite the scale of the challenge, researchers worldwide have shown interest in bioacoustics as a method to monitor wolves and this interest has been sustained in recent years as shown by recent publications (Barber‐Meyer et al., 2020; Cozzi et al., 2021; Garland et al., 2020; Hull et al., 2020; Papin et al., 2019, 2018; Russo et al., 2020; Sadhukhan et al., 2021). 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(2010) looked at various habitat and landscape characteristics that characterize wet meadows and retained three variables: green leaf biomass using the Normalized Difference Vegetation Index (NDVI), surface roughness and profile curvature. Areas with grasses and open canopy could be distinguished via their high NDVI values, indicating healthy vegetation as opposed to values closer to 0 for unhealthy vegetation and negative values for non-vegetated areas. Negative profile curvature indicated concave landscape that could retain water and a low roughness was synonymous with little landscape variability that characterize flat areas. These three variables were used to create a resource selection function (RSF) to predict rendezvous sites locations and Akaike’s Information Criterion (AIC) was used to determine the best model. This model can be used in a GIS with the following equation and the estimates for each parameter from the best model (Table 5): logit(p) = b0 + b1x1 + … + bkxk (1) where p: probability of being a rendezvous site, b1 … bk : estimated regression coefficients and, x1 … xk : values for vegetation and topography determined by a pixel by pixel analysis. 64 Table 5: Logistic regression parameters (SE) and odds ratios from the highest ranked model describing gray wolf rendezvous sites in Idaho, USA 1996-2006 (Ausband et al., 2010). Parameter Estimate SE Odds ratio Lower 95% CI Upper 95% CI Constant -13.589 4.28 Green leaf biomass 0.083 0.025 1.086 1.035 1.14 Profile curvature 0.492 0.211 1.636 1.081 2.475 Roughness -0.537 0.09 0.585 0.49 0.698 The Ausband et al. (2010) model has been validated over three years in an area of Idaho where wolves were also radio collared. The predictive map was successful as all 15 known packs were detected, as well as 20 out of 27 litters. Using the predictive map, only 11% (11,337 km2) of the study area was surveyed by pairs of technicians and this sampling effort required 260 person-days (Ausband et al., 2010). Subsequently, this technique has been implemented in other studies involving DNA collection in Idaho (Ausband et al., 2014; Stansbury et al., 2014; Stenglein et al., 2011, 2010) and in southwest Alberta (Ausband and Bassing, 2016; Bassing, 2017) to maximize detection by stratifying sampling. This predictive model, developed in an unharvested population of wolves, was still accurate after hunting was implemented in Idaho, as wolves did not modify their use of pup rearing habitat (Jacobs and Ausband, 2018). Data required to build a predictive map for the East Kootenays was obtained from open-source websites (Table 6). Layers required include Landsat imagery to create the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to assess profile curvature and roughness. Criteria used to select Landsat-8 imagery were dates between June and August 2017 to reflect peak vegetation growth and with less than 10% cloud cover. When this predictive map was built for Idaho (Ausband et al., 2010), the authors used a 15 m resolution. 65 The finest resolution available for the study area was 20 meters for the Canadian DEM and 30 meters for Landsat imagery and the American DEM. Additional layers such as lakes and rivers were obtained from DataBC and used as binary rasters to exclude certain features where wolf presence is unlikely despite habitat characteristics that fit the criteria for wet meadows. A buffer of 1 km was used around main road classes (highway, arterial, collector and local) for further exclusions (Jimenez, 1992; Matteson, 1992; Mech et al., 2003; Thiel et al., 1998). The map was binned into 10 equal area classes. Only contiguous patches of 1 hectare in the top three classes (Ausband and Bassing, 2016) were retained to generate the final predictive map (Figure 10). 66 Table 6: Data used to build the predictive map of rendezvous sites for the Flathead valley, British Columbia, Canada. Source and Layer Mapsheet or Path/Row Resolution Spatial reference Date https://open.canada.ca/ DEM (British Columbia and Alberta) CDEM (m) 82G, 82J, 82H, 82L, 82O, 82P 0.75 arc-second (approx. 20 m) GCS NAD83 CSRS Datum NAD83 CSRS https://earthexplorer.usgs.gov/ DEM (Montana) SRTM (m) 114, 115, 116 1 arc-second (approx. 30 m) GCS WGS84 Datum WGS84 2000-03-11 Landsat8 42/25, 42/26 30 m WGS84 UTM zone 11N Datum WGS84 2017-07-18 https://data.gov.bc.ca/ Wildlife Management Units UTM NAD83 zone 11N Datum NAD83 CSRS Municipalities polygons Roads Lakes Rivers Private land parcels Cities 67 Figure 10: Map of the top three classes of predicted rendezvous sites with a probability >70% for the Flathead valley, British Columbia, Canada. UTM NAD83 zone 11N, source: USGS, GeoGratis, DataBC. Finally, data driven pages were used to create a mapbook to facilitate fieldwork. A grid index of approximately 1:50,000 was used to emulate topographic maps, and a customized grid of 1.2649 x 1.2649 km was added to facilitate fieldwork. Indeed, as meadows can extend over several square kilometers, alternating blocks of 1.6 km2 (Ausband et al., 2010) were to be surveyed when sites are too large to be visited in their entirety. 68 Proper validation of the model is impossible as the only data regarding wolf populations in the East Kootenays comes from the camera traps that were deployed in the area during the previous two years and harvest information. These cameras were installed along trails to survey multiple species; therefore, they do not inform on wolf rendezvous sites. The usage of these traveling corridors does not necessarily indicate a rendezvous site in the vicinity. To locate rendezvous sites, we adopted the method used by Ausband et al. (2010) and applied elsewhere (Ausband and Bassing, 2016; Stansbury et al., 2014; Stenglein et al., 2010). Roads in areas with a >70% probability of rendezvous sites were driven at low speed (20 km/h) to look for signs such as tracks and scats. Elicited howling was used by imitating howls (3 set of 5 howls) and listening for replies to assess the presence of wolves at each site identified by the predictive map. If wolves replied, extensive effort was made to locate the rendezvous site. If no replies were heard, the periphery and trails leading to the rendezvous site were searched for 30-45 minutes looking for signs of presence and an attempt made to locate the activity center (i.e. bedding area with a high concentration of pups’ scats which are smaller than 2.5 cm in diameter). Sites were considered active when wolves were heard, observed or when several signs of presence observed such as heavily used trails leading to the site and more than 10 scats at the activity center. Because wet meadows can be very large, sites that cannot be surveyed entirely were to be surveyed in alternating blocks of 1.6 km2 and only contiguous patches of more than 1 ha were searched (Ausband et al., 2010).
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Biology
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Ford, Adam
Assessing passive recording of howls to measure wolf (Canis lupus) occupancy in the East Kootenays, British Columbia
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