SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.

SNAPSHOT USA is a multicontributor, long-term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22-5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: "adult caribou", "calf caribou", and "ghost caribou" (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96-0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers' annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.

Behavioral responses of blue-winged teal and northern shoveler to unmanned aerial vehicle surveys.

Unmanned aerial vehicles (UAVs) have become a popular wildlife survey tool. Most research has focused on detecting wildlife using UAVs with less known about behavioral responses. We compared the behavioral responses of breeding blue-winged teal (Spatula discors) (n = 151) and northern shovelers (Spatula clypeata) (n = 46) on wetlands flown over with a rotary DJI Matrice 200 quadcopter and control wetlands without flights. Using a GoPro camera affixed to a spotting scope, we conducted focal individual surveys and recorded duck behaviors for 30 minutes before, during, and 30 minutes after UAV flights to determine if ducks flushed or changed in specific activities. We also conducted scan surveys during flights to examine flushing and movement on the entire wetland. Between 24 April and 27 May 2020, we conducted 42 paired (control and flown) surveys. Both teal and shovelers increased proportion of time engaged in overhead vigilance on flown wetlands from pre-flight to during flight (0.008 to 0.020 and 0.006 to 0.032 of observation time, respectively). Both species left the wetland more frequently during flights than ducks on control wetlands. Despite similarities between species, we observed marked differences in time each species spent on active (e.g., feeding, courtship, swimming), resting, and vigilant behaviors during flights. Overall, teal became less active during flights (0.897 to 0.834 of time) while shovelers became more active during this period (0.724 to 0.906 of time). Based upon scan surveys, ducks flushed in 38.1% of surveys while control wetlands only had a single (2.4%) flush during the flight time. We found launch distance was the most important predictor of whether ducks swam for cover or away from the UAV which could result in inaccurate counts. Ducks appear aware of UAVs during flights, but minimal behavioral shifts suggest negative fitness consequences are unlikely.

Plasticity of Least Tern and Piping Plover nesting behaviors in response to sand temperature.

Birds that nest on the ground in open areas, such as Piping Plovers (Charadrius melodus) and Interior Least Terns (Sternula antillarum athalassos), are exposed to high temperatures in thermally stressful environments. As a result, some ground-nesting avian species have adapted behavioral strategies to maintain thermal regulation of eggs and themselves. We assessed the impact of sand temperature on shorebird nesting behaviors by installing video cameras and thermocouples at 52 Least Tern and 55 Piping Plover nests on the Missouri River in North Dakota during the 2014-2015 breeding seasons. Daily duration and frequency of shading behaviors exhibited a nonlinear relationship with temperature; therefore, we used segmented regressions to determine at what threshold temperature (mean temperature = 25.7⸰C for shading behavior daily frequency and mean temperature = 25.1⸰C for shading behavior daily duration) shorebird adults exhibited a behavioral response to rising sand temperatures. Daily nest attendance of both species decreased with increasing sand temperatures in our system. Frequency and duration of daily shading behaviors were positively correlated with sand temperatures above the temperature threshold. Piping Plovers exhibited more and longer shading behaviors above and below the temperature thresholds (below: frequency = 10.30 ± 1.69 se, duration = 7.29 min ± 2.35 se; above: frequency = 59.27 ± 6.87 se) compared to Least Terns (below: frequency = -1.37 ± 1.98 se, duration = -0.73 min ± 1.51 se; above: frequency = 31.32 ± 7.29 se). The effects of sand temperature on avian ground-nesting behavior will be critical to understand in order to adapt or develop recovery plans in response to climate change.

Kin grouping is insufficient to explain the inclusive fitness gains of conspecific brood parasitism in the common eider.

Conspecific brood parasitism allows females to exploit other females' nests and enhance their reproductive output. Here, we test a recent theoretical model of how host females gain inclusive fitness from brood parasitism. High levels of relatedness between host and parasitizer can be maintained either by: (a) kin recognizing and parasitizing each other as a form of cooperative breeding or (b) natal philopatry and nest site fidelity facilitating the formation of kin groups, thereby increasing the probability of parasitism between relatives nesting in close proximity. To address these two hypotheses we genotyped feathers and hatch membranes of common eiders (Somateria mollissima) from western Hudson Bay, Canada, using a noninvasive sampling methodology. We found that most instances of brood parasitism do result in inclusive fitness gains. Furthermore, females with failed nests moved an average of 492 m from their previous year's nest site, while successful females only moved an average of 13 m. Therefore, we observed host-parasite relatedness can occur at levels higher than would be expected by chance even in the absence of kin grouping, suggesting that closely related females nesting near one another is not essential to maintain high host-parasitizer relatedness. In addition, kin grouping is only a transient phenomenon that cannot occur every year due to the propensity for females of failed nests to nest farther away from their nest site in subsequent years than females with successful nests, which provides support for kin recognition as a more likely mechanism to maintain high host-parasitizer relatedness over time.

A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay.

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P < 0.0001) and shrub classes (F2,182 = 160.16, P < 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P < 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change.

Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys.

Unmanned aircraft systems (UAS) are relatively new technologies gaining popularity among wildlife biologists. As with any new tool in wildlife science, operating protocols must be developed through rigorous protocol testing. Few studies have been conducted that quantify the impacts UAS may have on unhabituated individuals in the wild using standard aerial survey protocols. We evaluated impacts of unmanned surveys by measuring UAS-induced behavioral responses during the nesting phase of lesser snow geese (Anser caerulescens caerulescens) in Wapusk National Park, Manitoba, Canada. We conducted surveys with a fixed-wing Trimble UX5 and monitored behavioral changes via discreet surveillance cameras at 25 nests. Days with UAS surveys resulted in decreased resting and increased nest maintenance, low scanning, high scanning, head-cocking and off-nest behaviors when compared to days without UAS surveys. In the group of birds flown over, head-cocking for overhead vigilance was rarely seen prior to launch or after landing (mean estimates 0.03% and 0.02%, respectively) but increased to 0.56% of the time when the aircraft was flying overhead suggesting that birds were able to detect the aircraft during flight. Neither UAS survey altitude nor launch distance alone in this study was strong predictors of nesting behaviors, although our flight altitudes (≥75 m above ground level) were much higher than previously published behavioral studies. Synthesis and applications: The diversity of UAS models makes generalizations on behavioral impacts difficult, and we caution that researchers should design UAS studies with knowledge that some minimal disturbance is likely to occur. We recommend flight designs take potential behavioral impacts into account by increasing survey altitude where data quality requirements permit. Such flight designs should consider a priori knowledge of focal species' behavioral characteristics. Research is needed to determine whether any such disturbance is a result of visual or auditory stimuli.

Sharp-Tailed Grouse Nest Survival and Nest Predator Habitat Use in North Dakota's Bakken Oil Field.

Recent advancements in extraction technologies have resulted in rapid increases of gas and oil development across the United States and specifically in western North Dakota. This expansion of energy development has unknown influences on local wildlife populations and the ecological interactions within and among species. Our objectives for this study were to evaluate nest success and nest predator dynamics of sharp-tailed grouse (Tympanuchus phasianellus) in two study sites that represented areas of high and low energy development intensities in North Dakota. During the summers of 2012 and 2013, we monitored 163 grouse nests using radio telemetry. Of these, 90 nests also were monitored using miniature cameras to accurately determine nest fates and identify nest predators. We simultaneously conducted predator surveys using camera scent stations and occupancy modeling to estimate nest predator occurrence at each site. American badgers (Taxidea taxus) and striped skunks (Mephitis mephitis) were the primary nest predators, accounting for 56.7% of all video recorded nest depredations. Nests in our high intensity gas and oil area were 1.95 times more likely to succeed compared to our minimal intensity area. Camera monitored nests were 2.03 times more likely to succeed than non-camera monitored nests. Occupancy of mammalian nest predators was 6.9 times more likely in our study area of minimal gas and oil intensity compared to the high intensity area. Although only a correlative study, our results suggest energy development may alter the predator community, thereby increasing nest success for sharp-tailed grouse in areas of intense development, while adjacent areas may have increased predator occurrence and reduced nest success. Our study illustrates the potential influences of energy development on the nest predator-prey dynamics of sharp-tailed grouse in western North Dakota and the complexity of evaluating such impacts on wildlife.

Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing.

High-throughput sequencing has been proposed as a method to genotype microsatellites and overcome the four main technical drawbacks of capillary electrophoresis: amplification artifacts, imprecise sizing, length homoplasy, and limited multiplex capability. The objective of this project was to test a high-throughput amplicon sequencing approach to fragment analysis of short tandem repeats and characterize its advantages and disadvantages against traditional capillary electrophoresis. We amplified and sequenced 12 muskrat microsatellite loci from 180 muskrat specimens and analyzed the sequencing data for precision of allele calling, propensity for amplification or sequencing artifacts, and for evidence of length homoplasy. Of the 294 total alleles, we detected by sequencing, only 164 alleles would have been detected by capillary electrophoresis as the remaining 130 alleles (44%) would have been hidden by length homoplasy. The ability to detect a greater number of unique alleles resulted in the ability to resolve greater population genetic structure. The primary advantages of fragment analysis by sequencing are the ability to precisely size fragments, resolve length homoplasy, multiplex many individuals and many loci into a single high-throughput run, and compare data across projects and across laboratories (present and future) with minimal technical calibration. A significant disadvantage of fragment analysis by sequencing is that the method is only practical and cost-effective when performed on batches of several hundred samples with multiple loci. Future work is needed to optimize throughput while minimizing costs and to update existing microsatellite allele calling and analysis programs to accommodate sequence-aware microsatellite data.