Monitoring Program and Assessment of Coyote Predation for Olympic Marmots

Witczuk, Julia J. M.S., Summer2007 Wildlife Biology MONITORING PROGRAM AND ASSESSMENT OF COYOTE PREDATION FOR OLYMPIC MARMOTS Chairperson: L. Scott Mills The Olympic marmot (Marmota olympus) is an endemic species to the Olympic Peninsula, Washington State. Although nearly all of its range is enclosed within Olympic National Park, declines and local extirpations of the species have been documented. The most plausible driver of the decline appears to be an increase in predator pressure. My thesis had two main objectives. First, I investigated the role of non-native coyotes (Canis latrans) in causing marmot mortality. Through park-wide carnivore scat analysis I determined the spatial extent of coyote predation on Olympic marmots and the magnitude of coyote predation relative to other carnivore species. I used mtDNA analysis of scats to determine carnivore species and microsatellite markers for individual coyote identification. Out of 958 carnivore scats collected, 84% came from coyotes and 10.3% contained marmots. The proportion of scats containing marmots was highly variable across studied regions, ranging from 3% to 34%. Among 79 scats with marmot remains for which predator species identification with mtDNA was successful, 85% arose from coyote, 10% from bobcat (Lynx rufus) and 5% from cougar (Puma concolor). Twelve out of 13 coyote individuals identified with genetic markers included marmots in their diet. Overall, occurrence of marmot remains in coyote scats observed could be considered high, especially if relatively low marmot densities are taken into account, supporting the potential for coyote predation to be the main driving factor of the observed marmot declines and extinctions. For my second objective, I designed a large scale, long-term monitoring program for marmot populations in Olympic National Park accounting for financial constraints. The monitoring program is designed to reflect extinction-recolonisation dynamics via park-wide occupancy sampling. The sampling design is based on annual surveys of a set of at least 25 randomly selected clusters (closely located groups of polygons with record of current or historical occupancy by marmots), and 15 additional polygons to test for colonisations

Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning

Effects of population and habitat characteristics on the accuracy and precision of wildlife aerial surveys results

Estimation of population abundance is one of the most difficult tasks in wildlife management. In case of forest−dwelling ungulates, none of the currently available survey methods is satisfying in terms of accuracy, precision, and cost−effectiveness. Therefore, we propose a new method of ungulate monitoring based on distance sampling and using unmanned aerial vehicles equipped with thermal infrared cameras. The method is potentially more reliable and cost−effective than conventional survey techniques. It also allows for aerial surveys in the dark when animals are most active. However, the method needs to be tested before wide−scale implementation in wildlife management practice. While the effects of sampling design and effort on accuracy and precision of abundance estimates are well recognized, the importance of population and habitat characteristics is often overlooked by wildlife managers. We used simulations to assess the effects of population size, animal aggregation, and habitat−depended detection probability on the accuracy and precision of wildlife aerial survey results. We created 1000 virtual populations defined by population density (2−22 individuals/100 ha), mean group size (1−6 individuals), and probability of animal detection during surveys (proportional to canopy cover, 30−60%). Animals were distributed on a virtual study area (5000 ha) according to randomly generated density distribution. Each population was subjected to 25 simulated surveys using the same design (39 transects grouped in three 2.0×2.5 km blocks). The transects covered 12% of the entire study area. We used conventional distance sampling to estimate abundance and generalized linear models to assess the effect of each parameter on the accuracy and precision of estimates. The estimation accuracy was mostly affected by the probability of detection (B=–0.75) and, to a lesser degree, by aggre− gation (B=–0.25) and population size (B=0.09). Precision was influenced by the aggregation (B=0.32) and population size (B=–0.26), while detection probability had a weaker effect (B=–0.11). Observed significant differences in quality of abundance estimates derived by the same survey design, but with differing population and habitat characteristics, indicate that each survey requires an individual approach. It is impossible to formulate general recommendations, e.g. concerning flight plan or area coverage. To achieve the required level of precision, while minimizing the survey costs, it is necessary to test alternative survey designs with the aid of computer simulations

Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study

The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be concentrated within small sampling blocks. Such a design is inferior to spatially unrestricted randomized designs available if operations beyond visual line of sight (BVLOS) are allowed. We used computer simulations to assess whether the VLOS rule affects the accuracy and precision of wildlife density estimates derived from drone collected data. We tested two alternative flight plans (VLOS vs. BVLOS) in simulated surveys of low-, medium- and high-density populations of a hypothetical ungulate species with three levels of effort (one to three repetitions). The population density was estimated using the ratio estimate and distance sampling method. The observed differences in the accuracy and precision of estimates from the VLOS and BVLOS surveys were relatively small and negligible. Only in the case of the low-density population (2 ind./100 ha) surveyed once was the VLOS design inferior to BVLOS, delivering biased and less precise estimates. These results show that while the VLOS regulations complicate survey logistics and interfere with random survey design, the quality of derived estimates does not have to be compromised. We advise testing alternative survey variants with the aid of computer simulations to achieve reliable estimates while minimizing survey costs

Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results

<p>Effective wildlife management and conservation require reliable assessments of animal abundance. However, no ungulate monitoring methods is entirely satisfying in terms of cost-effectiveness and accuracy. A new method combining unmanned aerial vehicles (drones) and thermal infrared (TIR) imaging may have great potential as a tool for ungulate surveys. Drones enable safe operations at low flying altitudes, and at night – a time that often offers the optimal conditions for wildlife monitoring. To assess the feasibility of the proposed method we used fixed-wing drones with TIR cameras to conduct test surveys in Drawieński National Park, Poland. We demonstrated that ungulate thermal signatures are visible both in leafless deciduous and in pine-dominated coniferous forests. Survey timing highly influenced the results – the best quality thermal images were obtained at sunrise, late evening, and at night. Our preliminary results indicated that thermal surveys from drones are a promising method for ungulate enumeration. We demonstrated that with ground resolution of ~10 cm it is possible to visibly distinguish large species (i.e. red deer) and achieve a good level of area coverage. The main challenges of the method are difficulties in species identification due to relatively low resolution of TIR cameras, regulations limiting drone operations to visual line of sight, and high dependence on weather.</p