Quantifying False Positives in Avian Survey Data

Imperfect detection is a known issue when conducting count-based surveys in wildlife studies. False positive detections, observed occurrences of individuals that truly are not present, are often assumed to not occur. This assumption can bias detection rates and create misleading results when calculating population estimates. Survey methods such as the dependent double-observer method are suggested to reduce the occurrence of false positives (Nichols et al. 2000). My study quantified and compared rates of false positives in a single-observer method and a dependent double-observer method using computer-generated auditory surveys. I categorized volunteer observers as either inexperienced or experienced and asked them to identify vocalizations of ten grassland songbird species native to central Montana. False positive rates of experienced observers declined from 0.095 in single-observer surveys to 0.032 in dependent double-observer surveys. False positive rates of inexperienced observers declined from 0.511 in single-observer surveys to 0.391 in dependent double-observer surveys. Further evaluation will provide information on the effectiveness of the dependent double-observer method in providing more precise and less biased population estimates

Quantifying False Positives in Avian Survey Data

Imperfect detection is a known issue with conducting wildlife surveys. False positive detections, where an individual is counted as present when it truly is not, are often assumed to not occur. This assumption can skew detection rates and create misleading results when calculating population estimates. Survey methods such as the dependent double-observer method developed by Nichols et al. (2000) are suggested to reduce the occurrence of false positives by using two collaborating observers. This study quantified and compared rates of false positives between a single-observer method and the dependent double-observer method. This was accomplished with auditory surveys of ten grassland songbird species native to central Montana. Both inexperienced and experienced volunteer observers were asked to listen to randomly-generated surveys containing the vocalizations of these ten songbirds and identify the species. The decrease in false positive rates using the dependent double-observer method is substantial. Further evaluation will provide information on the effectiveness of the dependent double-observer method in providing more precise and less biased population estimates

Novel Methods for Deriving Snow Data from Remote Cameras and Applications to Wildlife Habitat and Ungulate Management

Snow, in both its quantity and its dynamics, is a key driver of many geophysical and ecological processes and is well understood from a purely hydrologic perspective. However, snow as it affects wildlife habitat and survival is only understood very broadly despite its potential effects on thermoregulation, movement, foraging, and escape from predation over winter. This knowledge gap can largely be attributed to the lack of snow data at temporal and spatial scales meaningful to wildlife. Remote cameras are already widely used in wildlife research and potentially are a low-cost, low-maintenance option for collecting snow data at high spatial and temporal resolutions in complex forested terrain. My thesis explores how remote cameras can be used to collect snow and weather data and then applies these data to two wildlife habitat questions. I begin by asking what hydrometeorological data can be derived from remote camera images. Chapter 1A focuses on snow depth and a package I built in program R to measure snow depth without the use of permanent snow stakes deployed in the camera viewshed. The potential use of this code in distance sampling with remote cameras is heavily emphasized because it may be of interest to other users of the package. However, for this thesis, no distance sampling was performed; the code was only implemented in creating virtual snow stakes which could be used to measure snow depth in the camera images for Chapters 2 and 3. This R package provides a means for other camera studies to collect fine-scale snow depth data without potentially impacting wildlife behavior. Chapter 1B focuses on correcting air temperature measurements made by cameras and deriving precipitation phase from combined image data and temperature data. The temperature correction model gives researchers more confidence in the temperature measurements collected by their cameras. However, precipitation phase is complicated to discern because of the relatively low resolution of images and the effects of wind and canopy interception. My other two chapters use these methods and models to address two wildlife habitat questions. One, what biophysical conditions promote retention of snow in complex forested terrain? Using snow and temperature data derived from the remote cameras and biophysical data collected at the camera sites, I built a model predicting locations of snow refugia in complex forested terrains. Knowledge about late-season snow cover provides insight into how forests can be managed to promote snow retention and thus promote habitat for snow-dependent wildlife species. Two, how do snow characteristics and winter severity affect the movement and distribution of ungulates over winter? I built a model relating deer and elk detections at my cameras to snow depth and temperature from cameras and snow density and hardness from on-site measurements. Snow density and hardness are expected to change drastically to the possible detriment of ungulates, but these properties are not included in current winter severity indices; My model is the first attempt at including these snow properties to better define winter severity for ungulates in a changing climate.masters, M.S., Fish & Wildlife Sciences -- University of Idaho - College of Graduate Studies, 2022-1

Understanding the spatiotemporal distribution of snow refugia in the rain-snow transition zone of north-central Idaho

Knowledge of snow cover distribution and disappearance dates over a wide range of scales is imperative for understanding hydrological dynamics and for habitat management of wildlife species that rely on snow cover. Identification of snow refugia, or places with relatively late snow disappearance dates (SDDs) compared to surrounding areas, is especially important as climate change alters snow cover timing and duration. The purpose of this study was to increase understanding of snow refugia in complex terrain spanning the rain-snow transition zone at fine spatial and temporal scales. To accomplish this objective, we used remote cameras to provide relatively high temporal and spatial resolution measurements on snowpack conditions. We built linear models to relate SDDs at the monitoring sites to topoclimatic and canopy cover metrics. One model to quantify SDDs included elevation, aspect, and an interaction between canopy cover and cold-air pooling potential. High-elevation, north-facing sites in cold-air pools (CAPs) had the latest SDDs, but isolated lower-elevation points also exhibited relatively late potential SDDs. Importantly, canopy cover had a much stronger effect on SDDs in CAPs than in non-CAPs, indicating that best practices in forest management for snow refugia could vary across microtopography. A second model that included in situ hydroclimate observations (December – February (DJF) temperature and March 1 snow depth) indicated that March 1 snow depth had little impact on SDD at the coldest winter temperatures, and that DJF temperatures had a stronger effect on SDD at lower snow depths, implying that the relative importance of snowfall and temperature could vary across hydroclimatic contexts in their impact on snow refugia. This new understanding of factors influencing snow refugia can guide forest management actions to increase snow retention and inform management of snow-dependent wildlife species in complex terrain

Virtual snow stakes: a new method for snow depth measurement at remote camera stations

Abstract Remote cameras are used to study demographics, ecological processes, and behavior of wildlife populations. Cameras have also been used to measure snow depth with physical snow stakes. However, concerns that physical instruments at camera sites may influence animal behavior limit installation of instruments to facilitate collecting such data. Given that snow depth data are inherently contained within images, potential insights that could be made using these data are lost. To facilitate camera‐based snow depth observations without additional equipment installation, we developed a method implemented in an R package called edger to superimpose virtual measurement devices onto images. The virtual snow stakes can be used to derive snow depth measurements. We validated the method for snow depth estimation using camera data from Latah County, Idaho, USA in winter 2020–2021. Mean bias error between the virtual snow stake and a physical snow stake was 5.8 cm; the mean absolute bias error was 8.8 cm. The mean Nash Sutcliffe Efficiency score comparing the fit of the 2 sets of measurements within each camera was 0.748, indicating good agreement. The edger package provides researchers with a means to take critical measurements for ecological studies without the use of physical objects that could alter animal behavior, and snow data at finer scales can complement other snow data sources that have coarser spatial and temporal resolution