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Stoat (Mustela erminea) and weasel (M. nivalis) are hard to monitor as they are elusive of nature and leave few identifying marks in their surroundings. Stoat and weasel are both fully protected in Denmark and are thought to be widely distributed throughout the country. Despite this stoat and weasel were listed on the Danish Red List as Near Threatened in 2019, as their densities and population trends are unknown. Using a modified novel camera trapping device, the Double-Mostela, a wooden box comprising a tracking tunnel and two camera traps, we attempted to obtain density estimates based on identification of individual stoats and weasels. We deployed camera traps both inside Double-Mostela traps and externally in three different study areas in northern Zealand, Denmark, and tested commercial, American scent-based lures to attract stoat and weasel. We obtained very low seasonal trapping rates of weasel in two study areas, but in one study area, we obtained a seasonal trapping rate of stoat larger compared to another study using the Mostela. In one study area, both species were absent. We observed no effect of scent-based lures in attracting small mustelids compared to non-bait traps. Potential reasons behind low capture rates of weasel and stoat are suboptimal habitat placement and timing of deployment of the Double-Mostelas, land-use changes over the last 200 years, predation from larger predators, as well as unintended secondary poisoning with rodenticides. Due to the scarcity of weasel and stoat captures, we were unable to make density estimates based on identification of individuals; however, we identified potential features that could be used for identification and density estimates with more captures.

Stoat (Mustela erminea) and weasel (M. nivalis) are hard to monitor as they are elusive of nature and leave few identifying marks in their surroundings. Stoat and weasel are both fully protected in Denmark and are thought to be widely distributed throughout the country. Despite this stoat and weasel were listed on the Danish Red List as Near Threatened in 2019, as their densities and population trends are unknown. Using a modified novel camera trapping device, the Double-Mostela, a wooden box comprising a tracking tunnel and two camera traps, we attempted to obtain density estimates based on identification of individual stoats and weasels. We deployed camera traps both inside Double-Mostela traps and externally in three different study areas in northern Zealand, Denmark, and tested commercial, American scent-based lures to attract stoat and weasel. We obtained very low seasonal trapping rates of weasel in two study areas, but in one study area, we obtained a seasonal trapping rate of stoat larger compared to another study using the Mostela. In one study area, both species were absent. We observed no effect of scent-based lures in attracting small mustelids compared to non-bait traps. Potential reasons behind low capture rates of weasel and stoat are suboptimal habitat placement and timing of deployment of the Double-Mostelas, land-use changes over the last 200 years, predation from larger predators, as well as unintended secondary poisoning with rodenticides. Due to the scarcity of weasel and stoat captures, we were unable to make density estimates based on identification of individuals; however, we identified potential features that could be used for identification and density estimates with more captures.

Beginning in 2019, the Utah Division of Wildlife Resources (UDWR) has implemented a survey methodology to evaluate chukar numbers and production using game cameras set at water sources throughout the state. The camera survey allows data to be captured in a wide geographic area and provides data on the year’s production and a year-to-year index of abundance. Helicopter surveys were discontinued due to increasing cost and safety concerns related to low-level helicopter flight.

Numerous large vision models for natural images, such as SAM, Florence-2, and GPT-4, have achieved state-of-the-art (SOTA) performance, largely due to vast amounts of image and text data available online. Smaller models like EfficientSAM and CLIP have also shown the potential of achieving significant results with comparatively less data. However, real-world scientific problems, particularly in remote sensing, present unique challenges due to the complexity of the data and scarcity of annotations. These problems often require data from multiple sources, such as hyperspectral sensors on airplanes and multispectral sensors on satellites, which are expensive and time-consuming to acquire.This dissertation addresses the key question: how can large vision models be built and trained effectively under data constraints? The proposed solution involves integrating domain-specific knowledge into large vision models, specifically vision transformers, to optimize their performance and training efficiency. By incorporating core signal processing techniques, domain-specific knowledge is encoded as prior information, guiding the feature extraction process and refining randomly initialized queries via a query refiner module. This approach accelerates convergence with limited training data. Three key applications are explored: (1) methane detection in remote sensing from aerial imagery, (2) animal detection and classification in large grasslands for ecological studies, and (3) estimation of physiological signals such as ECG and ISTI for stress assessment in biomedical contexts. This research establishes an optimal methodology for embedding domain-specific knowledge into deep learning models, thereby enhancing performance in data-limited environments. It provides valuable insights for improving the applicability of vision transformer-based models across various domains, contributing to advancements in computer vision research and its practical real-world applications.

Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols. This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of deep learning for animal detection, localisation, recognition, and their combinations. The paper also discusses the challenges, limitations, and future research directions of this field. A key message from this review is the need for representative training data covering the various scenarios in which target animals may appear, image annotation difficulties, and the comparability of DL model results across studies. Many studies have focused on single species, locations, or images with a high density of common target species. Assessments of models are potentially biased from using a single test set; many studies lack metrics to evaluate model efficiency, feasibility, and generalizability, and there are uncertainties regarding the optimal number of training images and required ground sample distance (GSD) for different animal detection tasks in drone imagery. The potential applications of deep learning in wildlife monitoring, conservation, and ecological research using drone imagery are substantial. By enhancing the accuracy and efficiency of animal detection in imagery, this technology could contribute to the understanding and protecting animal populations. To expand the applicability of deep learning to divers

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014-2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around & Ouml;resund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates.

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014-2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around & Ouml;resund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates.

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014-2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around & Ouml;resund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates.

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014-2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around & Ouml;resund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates.

This master’s thesis investigates the application of advanced machine learning models for the identification and classification of Swedish predators using camera trap images. With the growing threats to biodiversity, there is an urgent need for innovative and non-intrusive monitoring techniques. This study focuses on the development and evaluation of object detection models, including YOLOv5, YOLOv8, YOLOv9, and Faster R-CNN, aiming to enhance the surveillance capabilities of Swedish predatory species such as bears, wolves, lynxes, foxes, and wolverines. The research leverages a dataset from the NINA database, applying data preprocessing and augmentation techniques to ensure robust model training. The models were trained and evaluated using various dataset sizes and conditions, including day and night images. Notably, YOLOv8 and YOLOv9 underwent extended training for 300 epochs, leading to significant improvements in performance metrics. The performance of the models was evaluated using metrics such as mean Average Precision (mAP), precision, recall, and F1-score. YOLOv9, with its innovative Programmable Gradient Information (PGI) and GELAN architecture, demonstrated superior accuracy and reliability, achieving an F1-score of 0.98 on the expanded dataset. The research found that training models on images captured during both day and night jointly versus separately resulted in only minor differences in performance. However, models trained exclusively on daytime images showed slightly better performance due to more consistent and favorable lighting conditions. The study also revealed a positive correlation between the size of the training dataset and model performance, with larger datasets yielding better results across all metrics. However, the marginal gains decreased as the dataset size increased, suggesting diminishing returns. Among the species studied, foxes were the least challenging for the models to detect and identify, while wolves presented more significant

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014-2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around & Ouml;resund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates.

This master’s thesis investigates the application of advanced machine learning models for the identification and classification of Swedish predators using camera trap images. With the growing threats to biodiversity, there is an urgent need for innovative and non-intrusive monitoring techniques. This study focuses on the development and evaluation of object detection models, including YOLOv5, YOLOv8, YOLOv9, and Faster R-CNN, aiming to enhance the surveillance capabilities of Swedish predatory species such as bears, wolves, lynxes, foxes, and wolverines. The research leverages a dataset from the NINA database, applying data preprocessing and augmentation techniques to ensure robust model training. The models were trained and evaluated using various dataset sizes and conditions, including day and night images. Notably, YOLOv8 and YOLOv9 underwent extended training for 300 epochs, leading to significant improvements in performance metrics. The performance of the models was evaluated using metrics such as mean Average Precision (mAP), precision, recall, and F1-score. YOLOv9, with its innovative Programmable Gradient Information (PGI) and GELAN architecture, demonstrated superior accuracy and reliability, achieving an F1-score of 0.98 on the expanded dataset. The research found that training models on images captured during both day and night jointly versus separately resulted in only minor differences in performance. However, models trained exclusively on daytime images showed slightly better performance due to more consistent and favorable lighting conditions. The study also revealed a positive correlation between the size of the training dataset and model performance, with larger datasets yielding better results across all metrics. However, the marginal gains decreased as the dataset size increased, suggesting diminishing returns. Among the species studied, foxes were the least challenging for the models to detect and identify, while wolves presented more significant

Starting in 2019 the Utah Division of Wildlife Resources has implemented a chukar survey methodology to evaluate chukar numbers and production using game cameras set on water sources throughout the state. The camera survey allows data to be captured in a wide geographic area and provide data on the year’s production and a year-to-year index of abundance. Helicopter surveys have been discontinued due to increasing cost and safety concerns related to low-level helicopter flight.

The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound recordings but are often difficult to create. Here, we evaluate the effectiveness of BirdNET, a new machine learning tool freely available for automated recognition and acoustic data processing, for correctly identifying and detecting two cryptic forest bird species. BirdNET precision was high for both the Coal Tit (Peripatus ater) and the Short-toed Treecreeper (Certhia brachydactyla), with mean values of 92.6% and 87.8%, respectively. Using the default values, BirdNET successfully detected the Coal Tit and the Short-toed Treecreeper in 90.5% and 98.4% of the annotated recordings, respectively. We also tested the impact of variable confidence scores on BirdNET performance and estimated the optimal confidence score for each species. Vocal activity patterns of both species, obtained using PAM and BirdNET, reached their peak during the first two hours after sunrise. We hope that our study may encourage researchers and managers to utilize this user-friendly and ready-to-use software, thus contributing to advancements in acoustic sensing and environmental monitoring.

Airborne acoustic surveillance would enable and ease several applications, including security surveillance, urban and industrial noise monitoring, rescue missions, and wildlife monitoring. Airborne surveillance with an acoustic camera mounted on an airship would provide the deployment flexibility and utility required by these applications. Nevertheless, and problematically for these applications, there is not a single acoustic camera mounted on an airship yet. We make significant advances towards solving this problem by designing and constructing an acoustic camera for direct mounting on the hull of a UAV airship. The camera consists of 64 microphones, a central processing unit, and software for data acquisition and processing dedicatedly developed for far-field low-level acoustic signal detection. We demonstrate a large-aperture mock-up camera operation on the ground, although all preparations have been made to integrate the camera onto an airship. The camera has an aperture of 2 m and has been designed for surveillance from a height up to 300 m, with a spatial resolution of 12 m.

Drones equipped with thermal cameras have recently become readily available, broadening the possibilities for monitoring wildlife. The European hare (Lepus europaeus) is a nocturnal mammal that is closely monitored in Denmark due to populations declining since the mid-1900s. The limitations of current population-assessment methods, such as, spotlight counts and hunting game statistics, could be overcome by relying on drone surveys with thermal imaging for population counts. The aim of this study was to investigate the use of a DJI Mavic 2 Enterprise Advanced drone with thermal imaging as a tool for monitoring the Danish hare population. Multiple test flights were conducted over agricultural areas in Denmark in spring 2022, testing various flight altitudes, camera settings, and recording methods. The test flights were used to suggest a method for identifying and counting hares. The applied use of this methodology was then evaluated through a case survey that had the aim of identifying and counting hares over an agricultural area of 242 ha. Hares could be detected with thermal imaging at flight altitudes up to 80 m, and it was possible to fly as low as 40 m without observing direct behaviorial changes. Thermal images taken at these altitudes also provided enough detail to differentiate between species, and animal body size proved to be a good species indicator. The case study supported the use of thermal imaging-based drone surveys to identify hares and conduct population counts, thus indicating the suggested methodology as a viable alternative to traditional counting methods.

In order to monitor wildlife populations in a manner that supports policy makers and natural resource managers, data must be collected using frameworks and methodologies that allow for comparisons between projects and across time. Though hunting statistics may represent a reliable data source for monitoring population trends in game species, a standardised framework for collecting and analysing this data has never been established in Europe, even within countries. Here we describe a case study on the use of hunting statistics in Spain in order to (i) describe the variability in big game statistics collection frameworks across mainland regions of Spain and (ii) propose a minimum common denominator for a standardised approach at the country level. The main differences in methodologies identified are that each region collects different variables, uses different spatial and temporal resolution, and follows different methodologies. We described spatial patterns by grouping regions based on similarities in the hunting data collection system and identified socio-economic factors as a potential driver of differences in methodologies among regions. Hunting effort-related variables and improved temporal resolution (to the event level) must be incorporated in order to achieve country-level standardisation of methodologies. The use of application software to collect information from the field in a standardised way is recommended, which necessitates engaging stakeholders as part of the monitoring process. Applications software should be designed intentionally, and only after clear objectives for the monitoring program have been defined. Making hunting data open access will improve collaboration and information transfer to scientific and professional sectors. Our recommendations can be adapted to other European countries, which would make hunting data more useful for population monitoring and wildlife policy-making at large spatial scales. Initiatives such as the “European Wildli

The Utah Division of Wildlife Resources (UDWR) is charged with providing the public with angling opportunities and conserving native aquatic species. The UDWR also operates a network of fish hatcheries that stock fish to help provide sport fish and conservation needs. Changes in water availability influence the ability of the UDWR to stock fish and manage sport and native fisheries. Long-term trend data shows that water availability is decreasing in Utah. For example, Natural Resource Conservation Service Snotel data from 1955-2020 has shown that average snowpack in Utah has decreased 20.5% during that time period with a decreasing trend noted at 58 out of the 63 (92%) recording sites across the state (USDA NRCS 2020). Regardless, water availability does not follow these trends perfectly and there are wetter than normal years and years of extreme drought. Utah's fisheries managers need to continue to operate hatcheries, provide sport fishing opportunities, and conserve native species through fluctuating water conditions. Snowpacks during late 2020 into early 2021 were among the lowest on record. Drought conditions persisted through the summers of 2021 and 2022. These conditions prompted the UDWR to take a number of steps to protect fisheries. In particular, emergency regulation changes that liberalized harvest limits were enacted on 25 waters in the state. Stocking cuts/modifications were made at over 50 waters. Drought conditions prompted fisheries managers to salvage green lineage Colorado River Cutthroat trout from Beaver Creek and move them into an isolation facility at the Fountain Green Hatchery. It takes approximately a year for water from snowpack to percolate into hatchery aquifers (varies by hatchery). Poor snowpacks in spring 2021 led to low flows at the Fountain Green Hatchery in spring 2022, which led to the need to stock some quotas of fish earlier than planned. Unfortunately, some stocking did not happen fast enough and approximately 40,000 rainbow tr

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

The Utah Division of Wildlife Resources (UDWR) is charged with providing the public with angling opportunities and conserving native aquatic species. The UDWR also operates a network of fish hatcheries that stock fish to help provide sport fish and conservation needs. Changes in water availability influence the ability of the UDWR to stock fish and manage sport and native fisheries. Long-term trend data shows that water availability is decreasing in Utah. For example, Natural Resource Conservation Service Snotel data from 1955-2020 has shown that average snowpack in Utah has decreased 20.5% during that time period with a decreasing trend noted at 58 out of the 63 (92%) recording sites across the state (USDA NRCS 2020). Regardless, water availability does not follow these trends perfectly and there are wetter than normal years and years of extreme drought. Utah's fisheries managers need to continue to operate hatcheries, provide sport fishing opportunities, and conserve native species through fluctuating water conditions. Snowpacks during late 2020 into early 2021 were among the lowest on record. Drought conditions persisted through the summers of 2021 and 2022. These conditions prompted the UDWR to take a number of steps to protect fisheries. In particular, emergency regulation changes that liberalized harvest limits were enacted on 25 waters in the state. Stocking cuts/modifications were made at over 50 waters. Drought conditions prompted fisheries managers to salvage green lineage Colorado River Cutthroat trout from Beaver Creek and move them into an isolation facility at the Fountain Green Hatchery. It takes approximately a year for water from snowpack to percolate into hatchery aquifers (varies by hatchery). Poor snowpacks in spring 2021 led to low flows at the Fountain Green Hatchery in spring 2022, which led to the need to stock some quotas of fish earlier than planned. Unfortunately, some stocking did not happen fast enough and approximately 40,000 rainbow tr

Historically, the Utah Division of Wildlife Resources (UDWR) managed with Tiger Muskellunge Esox masquinongy x E. lucius in Utah to provide effective biological controls for nuisance or overabundant fish populations in large reservoirs and to provide trophy fishing opportunities for Utah anglers fishing at larger lentic waters. However, achievement of desired management objectives has always been limited by the availability of fish for stocking. Although early Utah efforts to stock sufficient Tiger Muskellunge to meet management needs were reliant on fish purchased from other states (e.g., North Carolina, Iowa, and Nebraska), concerns regarding disease (viz., Viral Hemorrhagic Septicemia or VHS) and aquatic invasive species (AIS) led UDWR to begin pursuit of their own Tiger Muskellunge culture program in 2007. In the past, almost all of the anglers engaging in recreational fishing for Tiger Muskellunge in Utah were extremely specialized and often associated with fishing organizations like Muskies Inc. Specialized anglers have continued to represent the majority of anglers pursuing Tiger Muskellunge. However, the size and unique predatory behavior of this species has caught the attention of a broader array of angling interests in recent years. These less specialized anglers are seeking Tiger Muskellunge fishing experiences that differ from those traditionally exhibited by the trophy-oriented, highly-specialized angler. During the past decade managers have recognized the potential of Tiger Muskellunge as a more than a trophy fish or biological control in large reservoirs. Potential uses of Tiger Muskellunge as a management tool might include use in the (1) control of stunted salmonid populations in high mountain lakes, (2) recruitment of young anglers at community waters, (3) development of opportunity fisheries for anglers seeking fast action for exciting new species, or (4) support of Tiger Muskellunge stocking programs through the sale of conservation permits.

We stocked 2,500 juvenile tiger muskies in Jordanelle Reservoir this week! Tiger muskie numbers at Jordanelle were lower than we’d like, so we gave them a little boost by stocking these young fish into the reservoir. Tiger muskies are hybrid, sterile fish and apex predators. In Utah, we use them to biologically control the populations of other fish species — they serve an important management role in a number of our lakes and reservoirs.

Just under 50,000 fingerling Bonneville cutthroat were stocked into many streams along the Wasatch Front last week. Reintroduced beavers also helped restore fish and wildlife habitat at some remote streams, allowing biologists to reintroduce cutthroat trout into these newly-created beaver ponds. Many other species will benefit from these restoration efforts as well. Thanks to the many conservation partners that assisted with this beaver reintroduction project, such as the Rocky Mountain Elk Foundation, Safari Club International - SCI, Sportsmen for Fish and Wildlife, Sageland Collaborative - Formerly Wild Utah Project and the U.S. Forest Service.

A cutthroat trout’s journey from egg to catchable fish is remarkable. And, it’s a journey that’s changed substantially in recent decades. There are four subspecies of cutthroat trout in Utah. Thanks to hard work and the help of our committed conservation partners, they are all thriving. Here's a tribute to Utah's native cutthroat trout.

Stocked rainbow (RBT) and brook (BRK) trout made up the sport fisheries in the Box Creek reservoirs for several decades. Redside shiners were illegally introduced and have often maintained dense populations. Water fluctuation and competition with shiners provided challenges to trout survival and growth, resulting in generally marginal fisheries sustained by catchable-sized RBT and inconsistently available BRK. Evaluation of the fisheries was limited to angler reports until 2013, when regular monitoring was initiated with trend net surveys. Three surveys have been conducted since then and the current regional sampling strategy has prescribed netting surveys at the Box Creek reservoirs every five years. Following observation of a single large splake (apparently mistakenly stocked with a load of BRK) in UBC during the 2013 survey, managers recognized the potential benefit of stocking predators that could utilize redside shiners as forage. Half of the BRK quotas were converted to tiger trout because they are generally more easily caught by anglers than splake. The BRK quotas were eventually canceled after the 2016 survey found that tiger trout were performing well in LBC. Netting results were less favorable in UBC, though this may have been due to insufficient sampling effort. Requested quotas of RBT and tiger trout were adjusted to better reflect the relative size of the reservoirs, while a quota of splake was eventually added to UBC (Tables 1 and 2). In addition, excess Bonneville cutthroat trout (BCT) produced by the Manning Meadow brood were stocked in UBC in an attempt to add more potential production to the fishery. While the UBC fishery has continued to struggle, anglers have begun targeting large tiger trout in LBC and have caught fish up to eight pounds in size.

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

Health diagnostics of wildlife have historically relied on the evaluation of select serum biomarkers and the identification of a contaminant or pathogen burden within specific tissues as an indicator of a level of insult. However, these approaches fail to measure the physiological reaction of the individual to stressors, thus limiting the scope of interpretation. Gene-based health diagnostics provide an opportunity for an alternate, whole-system, or holistic assessment of health, not only in individuals or populations but potentially in ecosystems. Seabirds are among the most threatened marine taxonomic groups in the world, with ~25% of this species currently listed as threatened or considered of special concern; among seabirds, the penguins (Family Spheniscidae) are the most threatened seabird Family. We used gene expression to develop baseline physiological indices for wild penguins in the Falkland-Malvinas Islands, and captive zoo penguins. We identified the almost complete statistical separation of penguin groups (gentoo Detroit Zoo, gentoo Falkland-Malvinas Islands, rockhopper Detroit Zoo, and rockhopper Falkland-Malvinas Islands) based on gene expression profiles. Implementation of long-term longitudinal studies would allow for the assessment of temporal increases or decreases of select transcripts and would facilitate interpretation of the drivers of change.

The Nine-banded Armadillo (Dasypus novemcinctus) is a widespread burrowing species with an expanding geographic range across the southeastern and midwestern United States. Armadillos dig numerous, large burrows within their home ranges and these burrows are likely used by a diverse suite of wildlife species as has been reported for other burrowing ecosystem engineers such as Gopher Tortoises (Gopherus polyphemus), Desert Tortoises (Gopherus agassizi), and Black-tailed Prairie Dogs (Cynomys ludovicianus). We used motion-triggered game cameras at 35 armadillo burrows in 4 ecoregions of Arkansas and documented 19 species of mammals, 4 species of reptile, 1 species of amphibian, and 40 species of bird interacting with burrows. Bobcat (Lynx rufus), Coyote (Canis latrans), Eastern Cottontail (Sylvilagus floridanus), Gray Fox (Urocyon cinereoargenteus), Gray Squirrel (Sciurus carolinensis), Northern Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), and unidentified rodents (mice and rats) were documented using burrows in all four ecoregions. We documented wildlife hunting, seeking shelter, rearing young in, and taking over and modifying armadillo burrows. The rate of use was highest in the Mississippi Alluvial Valley, a landscape dominated by agriculture, where natural refugia may be limited and rodents are abundant. Armadillo burrows are clearly visited and used by numerous wildlife species to fulfill various life stage requirements, and this list will likely expand if more attention is devoted to understanding the role of armadillos burrows. Armadillos are important ecosystem engineers, and their ecological role warrants more investigation and attention as opposed to only being viewed and managed as agricultural and garden pests.

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

Hybridisation between wild and domestic taxa raises complex questions for conservation. Genetic advances offer new methods for hybrid identification, yet social and cultural factors can influence study design, and the interpretation, application, and communication of results. A relevant illustration is hybridisation between domestic dogs (Canis lupus familiaris) and wild canids, such as grey wolves (C. lupus). For regional European monitoring programs in areas with expanding wolf populations, priorities include shared genetic markers and inclusion of all relevant reference populations to ensure dispersing wolves are identified as such and not classified as wolf-dog hybrids, which may cause harmful management decisions. Beyond technical developments, hybrid research and conservation management can benefit from improved integration of legal and policy perspectives, recognition of phenotypic traits as broadly unreliable for identification, and attention to the drivers of, and responses to, evolution in human-dominated landscapes. Additionally, the proliferation of unsubstantiated reports about hybrids in popular and social media shows that communication based on verified findings of hybridisation is essential. Hybridisation requires more constructive discussion on how to balance potentially competing conservation objectives, and the integration of multidisciplinary perspectives. These encompass the welfare of individual animals and preservation of historical predator-prey relationships. Conservation measures centred on preserving the ecological function of wild canids likely offer the most sustainable prospects but require improved understanding of the extent to which their behavioural ecology might differ from that of hybrids. Accurate genetic identification is required to fill this critical knowledge gap, advance public discourse, and initiate relevant conservation actions.

Historically, the Utah Division of Wildlife Resources (UDWR) managed with Tiger Muskellunge Esox masquinongy x E. lucius in Utah to provide effective biological controls for nuisance or overabundant fish populations in large reservoirs and to provide trophy fishing opportunities for Utah anglers fishing at larger lentic waters. However, achievement of desired management objectives has always been limited by the availability of fish for stocking. Although early Utah efforts to stock sufficient Tiger Muskellunge to meet management needs were reliant on fish purchased from other states (e.g., North Carolina, Iowa, and Nebraska), concerns regarding disease (viz., Viral Hemorrhagic Septicemia or VHS) and aquatic invasive species (AIS) led UDWR to begin pursuit of their own Tiger Muskellunge culture program in 2007. In the past, almost all of the anglers engaging in recreational fishing for Tiger Muskellunge in Utah were extremely specialized and often associated with fishing organizations like Muskies Inc. Specialized anglers have continued to represent the majority of anglers pursuing Tiger Muskellunge. However, the size and unique predatory behavior of this species has caught the attention of a broader array of angling interests in recent years. These less specialized anglers are seeking Tiger Muskellunge fishing experiences that differ from those traditionally exhibited by the trophy-oriented, highly-specialized angler. During the past decade managers have recognized the potential of Tiger Muskellunge as a more than a trophy fish or biological control in large reservoirs. Potential uses of Tiger Muskellunge as a management tool might include use in the (1) control of stunted salmonid populations in high mountain lakes, (2) recruitment of young anglers at community waters, (3) development of opportunity fisheries for anglers seeking fast action for exciting new species, or (4) support of Tiger Muskellunge stocking programs through the sale of conservation permits.

We stocked 2,500 juvenile tiger muskies in Jordanelle Reservoir this week! Tiger muskie numbers at Jordanelle were lower than we’d like, so we gave them a little boost by stocking these young fish into the reservoir. Tiger muskies are hybrid, sterile fish and apex predators. In Utah, we use them to biologically control the populations of other fish species — they serve an important management role in a number of our lakes and reservoirs.

Just under 50,000 fingerling Bonneville cutthroat were stocked into many streams along the Wasatch Front last week. Reintroduced beavers also helped restore fish and wildlife habitat at some remote streams, allowing biologists to reintroduce cutthroat trout into these newly-created beaver ponds. Many other species will benefit from these restoration efforts as well. Thanks to the many conservation partners that assisted with this beaver reintroduction project, such as the Rocky Mountain Elk Foundation, Safari Club International - SCI, Sportsmen for Fish and Wildlife, Sageland Collaborative - Formerly Wild Utah Project and the U.S. Forest Service.

A cutthroat trout’s journey from egg to catchable fish is remarkable. And, it’s a journey that’s changed substantially in recent decades. There are four subspecies of cutthroat trout in Utah. Thanks to hard work and the help of our committed conservation partners, they are all thriving. Here's a tribute to Utah's native cutthroat trout.

Passive acoustic monitoring is a powerful tool for monitoring vocally active taxa. Automated signal recognition software reduces the expert time needed for recording analyses and allows researchers and managers to manage large acoustic datasets. The application of state-of-the-art techniques for automated identification, such as Convolutional Neural Networks, may be challenging for ecologists and managers without informatics or engineering expertise. Here, we evaluated the use of AudioMoth — a low-cost and open-source sound recorder — to monitor a threatened and patchily distributed species, the Eurasian bittern (Botaurus stellaris). Passive acoustic monitoring was carried out across 17 potential wetlands in north Spain. We also assessed the performance of BirdNET — an automated and freely available classifier able to identify over 3000 bird species — and Kaleidoscope Pro — a user-friendly recognition software — to detect the vocalizations and the presence of the target species. The percentage of presences and vocalizations of the Eurasian bittern automatically detected by BirdNET and Kaleidoscope software was compared to manual annotations of 205 recordings. The species was effectively recorded up to distances of 801–900 m, with at least 50% of the vocalizations uttered within that distance being manually detected; this distance was reduced to 601–700 m when considering the analyses carried out using Kaleidoscope. BirdNET detected the species in 59 of the 63 (93.7%) recordings with known presence of the species, while Kaleidoscope detected the bittern in 62 recordings (98.4%). At the vocalization level, BirdNet and Kaleidoscope were able to detect between 76 and 78%, respectively, of the vocalizations detected by a human observer. Our study highlights the ability of AudioMoth for detecting the bittern at large distances, which increases the potential of that technique for monitoring the species at large spatial scales. According to our results, a single AudioMoth c

Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway.

Starting in 2019 the Utah Division of Wildlife Resources has implemented a chukar survey methodology to evaluate chukar numbers and production using game cameras set on water sources throughout the state. The camera survey allows data to be captured in a wide geographic area and provide data on the year’s production and a year-to-year index of abundance. Helicopter surveys have been discontinued due to increasing cost and safety concerns related to low-level helicopter flight.

Stocked rainbow (RBT) and brook (BRK) trout made up the sport fisheries in the Box Creek reservoirs for several decades. Redside shiners were illegally introduced and have often maintained dense populations. Water fluctuation and competition with shiners provided challenges to trout survival and growth, resulting in generally marginal fisheries sustained by catchable-sized RBT and inconsistently available BRK. Evaluation of the fisheries was limited to angler reports until 2013, when regular monitoring was initiated with trend net surveys. Three surveys have been conducted since then and the current regional sampling strategy has prescribed netting surveys at the Box Creek reservoirs every five years. Following observation of a single large splake (apparently mistakenly stocked with a load of BRK) in UBC during the 2013 survey, managers recognized the potential benefit of stocking predators that could utilize redside shiners as forage. Half of the BRK quotas were converted to tiger trout because they are generally more easily caught by anglers than splake. The BRK quotas were eventually canceled after the 2016 survey found that tiger trout were performing well in LBC. Netting results were less favorable in UBC, though this may have been due to insufficient sampling effort. Requested quotas of RBT and tiger trout were adjusted to better reflect the relative size of the reservoirs, while a quota of splake was eventually added to UBC (Tables 1 and 2). In addition, excess Bonneville cutthroat trout (BCT) produced by the Manning Meadow brood were stocked in UBC in an attempt to add more potential production to the fishery. While the UBC fishery has continued to struggle, anglers have begun targeting large tiger trout in LBC and have caught fish up to eight pounds in size.

This dissertation presents the application of state-of-art image classification and object detection techniques to two projects in the areas of wildlife monitoring and cancer cell characterization. The following presents a summary of the problem statement, solution approach, and major contributions for each of the two projects. In the wildlife monitoring project, an automated vision system is proposed for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive and labor-intensive to conduct, trail-camera imaging is a comparatively non-intrusive method for capturing wildlife activity. However, given the large volume of images produced from trail cameras, manual analysis of the images remains time-consuming and inefficient. For this purpose, a two-stage deep convolutional neural network pipeline is implemented to find animal-containing images in the first stage and then process these images to detect birds in the second stage. The animal classification system classifies animal images with 93% sensitivity and 96% specificity. The bird detection system achieves better than 93% sensitivity, 92% specificity, and 68% average Intersection-over-Union rate. This dissertation also addresses post-deployment issues related to data drift for the animal classification system as image features vary with seasonal changes. This system utilizes an automatic retraining algorithm to detect data drift and to update the system when necessary. Moreover, two statistical experiments are presented to explain the prediction behavior of the animal classification system. These experiments investigate the cues that steer the system towards a particular decision. Statistical hypothesis testing demonstrates that the presence of an animal in the input image significantly contributes to the system’s decisions. The cell characterization study investigates the automatic dete, Embargo status: Restricted until 06/2022. To request the author grant access, click on the PDF link to the left.

Existing research recognises the critical role played by Unmanned Aerial Vehicles (UAVs) (also referred to as drones) to numerous civilian applications. Typical drone applications include surveillance, wireless communication, agriculture, among many others. One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellular networks to victims and rescue teams in disaster-affected areas. However, few studies have investigated the optimal deployments of multiple drone-cells with limited backhaul communication distances. In addition, the use of autonomous drones as flying interactors for many real-life applications has not been sufficiently discussed. With superior maneuverability, drone-enabled autonomous aerial interacting can potentially be used on shark attack prevention and animal herding. Nevertheless, previous studies of autonomous drones have not dealt with such applications in much detail. This thesis explores the solutions to all the mentioned research questions, with a particular focus on the deployment and navigation of the drones. First, we provide one of the first investigations into reducing the negative impacts of wildlife monitoring drones by navigation control. Second, we study the optimal placement of a group of drone-cells with limited backhaul communication ranges, aims to maximise the number of served users. Third, we propose a novel method named ‘drone shark shield’, which uses communicating autonomous drones to interve

Subnivean life is an important part of the Arctic ecosystem but it has been little explored. Long, harsh winters in addition to remoteness have made direct studies in these hardly accessible areas very expensive and extremely difficult. To tackle this problem, a low-power autonomous camera system (called ArcÇav) is developed for monitoring small mammals beneath the snow in the Canadian Arctic. ArcÇav is composed of several components, including a digital camera, a single board computer, a microcontroller board, and a motion detection sensor. A limited energy source, very cold temperatures, darkness, and a very long recording period (several months) are major challenges that ArcÇav is designed to deal with. The performance of the developed system is evaluated in a real situation in the High Arctic. The field results show that ArcÇav can function well for an extended period of time on a battery at very low temperatures during the arctic winters. To the best of our knowledge, this is the first time that life under snow has been filmed by a camera trap in the Arctic during winter. ArcÇav equips ecologists with a new means to explore and study subnivean life remotely. These observations can provide a foundation to answer some of questions that have puzzled animal ecologists for decades.

Chaco Chachalaca (Ortalis canicollis) is a declining Neotropical bird, for which our current knowledge about its natural history is very limited. Here, we evaluated for first time the utility of passive acoustic monitoring, coupled with automated signal recognition software, to monitor the Chaco Chachalaca, described the vocal behavior of the species across the diel and seasonal cycle patterns, and proposed an acoustic monitoring protocol to minimize error in the estimation of the vocal activity rate. We recorded over a complete annual cycle at three sites in the Brazilian Pantanal. The species was detected on 99% of the monitoring days, proving that this technique is a reliable method for detecting the presence of the species. Chaco Chachalaca was vocally active throughout the day and night, but its diel activity pattern peaked between 0500 and 0900. The breeding season of Chaco Chachalaca in the Brazilian Pantanal, based on seasonal changes in vocal activity, seems to occur during the last months of the dry season, with a peak in vocal activity between August and October. Our results could guide future surveys aiming to detect the presence of the species, both using traditional or acoustic surveys, or to evaluate changes in population abundance using passive acoustic monitoring, for which recorders should be left in the field for a minimum period of nine days to obtain a low-error estimate of the vocal activity of the species. Our results suggest that passive acoustic monitoring might be useful, as a complementary tool to field studies, for monitoring other cracids, a family with several threatened species that are reluctant to human presence.

Cue counting is a method developed for estimating vocally active wildlife density by dividing the density of cues (number of cues per unit area surveyed per unit time) by the average cue rate (ACR) at which individuals vocalize. It has been used successfully to estimate whale density using passive acoustic monitoring, but its efficacy has had limited testing in birds. We tested whether cue counting can be used to infer bird abundance using autonomous recording units and estimated the minimum effort required to obtain a reliable cue rate at individual and population levels. We recorded Dupont's Lark (Chersophilus duponti) vocalizations at 31 sites where traditional field censuses were also performed. We estimated the ACR using three methodologies: directional recordings, recordings from an online database of bird sounds (xeno-canto), and behavioral field studies. The ACRs estimated using directional recordings and behavioral field studies were similar, and bird numbers were over and underestimated by 0.8 and 10%, respectively (74–77% of the sampling sites were well estimated). However, the ACR estimated using xeno-canto recordings was much higher than those estimated using the other two methods, and bird numbers were underestimated by 41%. We also performed a cost-effectiveness assessment of the number of individuals and recording durations needed to optimize the estimation of a reliable ACR. We found that ACR estimates were more efficient if long (25 min) recordings were used when < 4 males were recorded, whereas 5-min recordings were more efficient for ≥ 20 males. We conclude that cue counting can be useful to infer bird density around recorders but requires an accurate measure of the ACR. Further research should evaluate the effectiveness of passive cue counting on a large number of species and under different circumstances., Le comptage des détections est une méthode qui a été élaborée pour estimer la densité de la faune active vocalement en divisant la densité de détections (nombre de détections par unité de surface étudiée par unité de temps) par le taux moyen de détections (TMD) auquel les individus chantent ou crient. Cette méthode a été utilisée avec succès pour calculer la densité des baleines à l'aide d'un suivi sonore passif, mais son efficacité a peu été testée chez les oiseaux. Nous avons testé si le comptage des détections pouvait être utilisé pour déduire l'abondance des oiseaux en utilisant des enregistreurs automatisés et avons calculé l'effort minimum requis pour obtenir un taux de détections fiable au niveau des individus et des populations. Nous avons enregistré les manifestations sonores du Sirli de Dupont (Chersophilus duponti) sur 31 sites où des recensements traditionnels ont également été effectués. Nous avons calculé le TMD de trois façons : à partir d'enregistrements directionnels, d'enregistrements provenant d'une base de données de cris et de chants d'oiseaux accessible en ligne (xeno-canto) et d'études comportementales sur le terrain. Les TMD calculés à l'aide d'enregistrements directionnels et d'études comportementales étaient similaires, et le nombre d'oiseaux était surestimé et sous-estimé de 0,8 et 10 %, respectivement (la mesure de 74-77 % des sites d'échantillonnage était juste). Cependant, le TMD calculé à l'aide d'enregistrements de xeno-canto était beaucoup plus élevé que ceux obtenus au moyen des deux autres méthodes, et le nombre d'oiseaux était sous-estimé de 41 %. Nous avons également réalisé une évaluation coût-efficacité du nombre d'individus et des durées d'enregistrement nécessaires pour optimiser le calcul d'un TMD fiable. Nous avons constaté que la mesure du TMD était meilleure si des enregistrements longs (25 min) étaient utilisés lorsque < 4 mâles étaient enregistrés, tandis que des enregistrements de 5 min étaient plus efficaces pour ≥ 20

Population estimates are commonly generated and used in conservation science. All estimates carry inherent uncertainty, but little attention has been given to when and how this uncertainty limits their use. This requires an understanding of the specific purposes for which population estimates are intended, an assessment of the level of uncertainty each purpose can tolerate, and information on current uncertainty. We conducted a review and meta-analysis for a widespread group of seabirds, the petrels, to better understand how and why population estimates are being used. Globally petrels are highly threatened, and aspects of their ecology make them difficult to survey, introducing high levels of uncertainty into population estimates. We found that by far the most common intended use of population estimates was to inform status and trend assessments, while less common uses were trialling methods to improve estimates and assessing threat impacts and conservation outcomes. The mean coefficient of variation for published estimates was 0.17 (SD = 0.14), with no evidence that uncertainty has been reduced through time. As a consequence of this high uncertainty, when we simulated declines equivalent to thresholds commonly used to trigger management, only 5% of studies could detect significant differences between population estimates collected 10 years apart for populations declining at a rate of 30% over three generations. Reporting of uncertainty was variable with no dispersion statistics reported with 38% of population estimates and most not reporting key underlying parameters: nest numbers/density and nest occupancy. We also found no correlation between uncertainty in petrel population estimates and either island size, body size or species threat status – potential predictors of uncertainty. Key recommendations for managers are to be mindful of uncertainty in past population estimates if aiming to collect contemporary estimates for comparison, to report uncertainty clea

Native Bonneville cutthroat trout (BCT) were confirmed 98% pure in Mammoth Creek in 2012. The Mammoth Creek BCT Restoration Project was subsequently formulated with the goal of removing nonnative trout – primarily brook trout – from the Mammoth Creek drainage upstream of Mammoth Spring. Activities during 2015-2019 focused on nonnative trout removal in Mammoth Creek's tributaries. In 2020, the final phase of the project was commenced with nonnative removal in main stem Mammoth Creek and the Meadow Lake system. Salvage of remnant BCT was conducted during the 2020 treatment to maintain the genetic representation of the Mammoth Creek population. Drought-related complications experienced during rotenone application were mitigated by experienced crew. Coordination with private landowners presented a challenge to treatment logistics, but those efforts resulted in valuable cooperative relationships that facilitated BCT conservation. The final treatments of the main stem and Meadow Lake drainage were conducted in 2021, followed by introduction of BCT throughout the drainage. This report details those activities conducted in 2021.

The fishery in Barney Lake is regularly monitored through trend net surveys and the Southern Region Sampling Strategy has prescribed netting surveys every five years. In 2011, a new net design recommended by the American Fisheries Society (AFS) was introduced in trend net surveys at most Southern Region waters, including Barney Lake. This design was intended to reduce catch bias generated by graduated mesh nets, which “lead” fish into the net. In most waters where they have been employed, the AFS-style nets have yielded about 50% of the trout catch rates of the older style nets that were used by UDWR for many years. The new net design has been employed just twice at Barney Lake, so new trends in catch are still being evaluated. This report summarizes results of the 2020 spring trend net survey at Barney Lake, with comparisons to results of previous surveys.

Subnivean life is an important part of the Arctic ecosystem but it has been little explored. Long, harsh winters in addition to remoteness have made direct studies in these hardly accessible areas very expensive and extremely difficult. To tackle this problem, a low-power autonomous camera system (called ArcÇav) is developed for monitoring small mammals beneath the snow in the Canadian Arctic. ArcÇav is composed of several components, including a digital camera, a single board computer, a microcontroller board, and a motion detection sensor. A limited energy source, very cold temperatures, darkness, and a very long recording period (several months) are major challenges that ArcÇav is designed to deal with. The performance of the developed system is evaluated in a real situation in the High Arctic. The field results show that ArcÇav can function well for an extended period of time on a battery at very low temperatures during the arctic winters. To the best of our knowledge, this is the first time that life under snow has been filmed by a camera trap in the Arctic during winter. ArcÇav equips ecologists with a new means to explore and study subnivean life remotely. These observations can provide a foundation to answer some of questions that have puzzled animal ecologists for decades.

Knowledge about the connectivity among natural populations is essential to identify management units for effective conservation actions. Conservation-minded management has led to the recovery of large carnivore populations in northern Europe, possibly restoring connectivity between the two separated, but expanding brown bear (Ursus arctos) populations on the Scandinavian peninsula to the west and Karelia, a part of the large Eurasian population, to the east. The degree of connectivity between these populations has been poorly understood, therefore we investigated the extent of connectivity between the two populations using autosomal microsatellites and Y chromosome haplotypes in 924 male bears (the dispersing sex), sampled during a period of 12 years (2005–2017) across the transborder area where these two populations meet. Our results showed that the two populations are not genetically isolated as reported in earlier studies. We detected recent asymmetrical gene flow at a rate (individuals per generation) of 4.6–5.5 (1%) from Karelia into Scandinavia, whereas the rate was approximately 27.1–34.5 (8%) in the opposite direction. We estimated historical gene flow of effective number of migrants to be between 1.7 and 2.5 between the populations. Analyses of Y chromosome markers supported these results. Successful recovery and expansion of both populations led to the restoration of connectivity, however, it is asymmetric, possibly due to different recovery histories and population densities. By aligning monitoring between neighboring countries, we were able to better understand the biological processes across the relevant spatial scale.