Genetic Structure of Northern Bobwhites in Northeast Mississippi and Southwest Tennessee

Precipitous declines in northern bobwhite (Colinus virginianus) populations across most of the natural range may increase susceptibility to genetic isolation, restrict gene flow among subpopulations, and exacerbate vulnerability to catastrophic stochastic processes. We characterized the level of genetic variability of 223 individual bobwhites representing 4 disjunct populations in northeast Mississippi and southwest Tennessee in 2002. Analyses at 8 microsatellite loci suggested observed heterozygosity was lower than expected but showed no significant heterozygosity excess. Estimates of FIS coefficients were positive in each subpopulation, but low overall, suggesting only minor loss in heterozygosity over the entire population. Gene diversity was high and genetic differentiation within and among subpopulations and isolation by distance effects were minimal, suggesting adequate levels of gene flow. We suggest, despite population losses, gene flow is maintained among subpopulations, which may reflect the bobwhite’s ability to disperse successfully in the agricultural landscape in this region. Maintenance of gene flow across seemingly inhospitable landscapes suggests focal area management directives may enhance population sustainability. Greater understanding of the genetic structure of northern bobwhite populations on larger geographic scales and across the species’ range is paramount to population recovery

Aquatic habitat changes within the channelized and impounded Arkansas River, Arkansas, USA

River-wide changes in morphologic character following channelization and impoundment alter the occurrence and distribution of surface water and available habitats for aquatic organisms. Quantifying patterns of creation, redistribution or disappearance of habitats at river-wide and decadal spatiotemporal scales can promote understanding regarding trajectories of different habitat types following alteration and prospects of direct habitat enhancement projects within altered alluvial rivers. Newly available remote-sensing tools and databases may improve detection of river-wide changes in habitat through time. We used a combination of remote-sensing data and generalized linear models to assess changes in surface water coverage from 1984 to 2015 among aquatic habitats of 496 km of the Arkansas River within Arkansas, USA. Changes through time in surface area of permanent and episodically inundated areas — and thus the availability of aquatic habitat — were variable along the river. Overall, the river lost a total 2.1% of permanent and 12.1% of episodic water surface area. The general trend of loss of off-main-channel habitat and increased coverage of permanent water along main-channel habitats may indicate a long-term transition (i.e. ramp-type disturbance) within areas of the Arkansas River where backwaters are transitioning to terrestrial environments, and habitat heterogeneity in the main channel is decreasing. As such, a decadal-scale change of channel form and backwater habitats may be the dominant pattern with limited regeneration of diverse habitat types. Understanding changes to permanent and episodic water availability may aid predictions regarding ecological effects of channelization and impoundments, including both increases and decreases in riverine productivity, biotic diversity and population abundances through space and time. Water resource managers and biologists can use information regarding river-wide changes in habitat availability obtained through remote sensing data to direct river management practices, including dredging and side-channel construction, and to assess ecological responses to such changes

Relationships between survival and habitat suitability of semi- aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche-based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi-aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30-m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent-predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability-fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed

Release of Pen-reared Bobwhites: Potential Consequences to the Genetic Integrity of Resident Wild Populations

In response to low encounter rates with wild northern bobwhite (Colinus virginianus; hereafter bobwhite) during bird dog field trials at Ames Plantation in Tennessee, a large-scale release program of pen-reared bobwhites was implemented in fall 2002. To evaluate genetic effects of pen-reared releases on wild populations, we monitored survival of pen-reared and wild bobwhites from fall release of pen-reared bobwhites through the breeding season and collected feather samples from wild, pen-reared, and free-ranging juvenile bobwhites following the first breeding season after the initial release. We used genotypes from 6 polymorphic microsatellite loci to measure genetic diversity and conduct population assignment tests. Wild bobwhites experienced greater fallspring and annual survival than pen-reared bobwhites; however, pen-reared bobwhites experienced greater fall-spring and annual survival than reported in most other studies. Genetic diversity, number of alleles, and allelic richness were greatest in the wild, intermediate in the F1 generation, and lowest in the pen-reared populations. Likelihood analysis and cluster analysis indicated 20.4% and 33.6%, respectively, of juveniles captured after the first breeding season following release were ambiguous in population assignment; suggesting successful reproduction between wild and pen-reared individuals. These results suggest that large-scale releases of pen-reared bobwhite may result in negative impacts on genetic integrity of resident wild populations

Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals

Detection Rates of Northern Bobwhite Coveys Using a Small Unmanned Aerial System-Mounted Thermal Camera

The northern bobwhite (Colinus virginianus; hereafter, bobwhite) requires intensive monitoring to evaluate management efforts and determine harvest rates. However, traditional monitoring techniques (e.g., covey-call surveys) are labor-intensive and imprecise. Small unmanned aerial systems (sUASs) mounted with thermal cameras have demonstrated promise for monitoring multiple avian species and could provide a less intensive and more effective approach to monitoring bobwhite coveys, assuming coveys produce a recognizable heat signature. To assess sUAS monitoring, we evaluated the influence of bobwhite covey size (3, 6, and 12) and cover type (grass, shrub, and forest) on covey detectability by a sUAS equipped with a thermal camera. We hypothesized that forest would have the lowest covey detection due to trees obstructing detection of the thermal signature and that larger covey size would improve covey detection due to the formation of larger, more visibly distinct thermal signatures. We placed groups of known-size, pen-reared bobwhites in steel mesh cages (3, 6, and 12 individuals/cage) in 3 vegetation types (grass, shrub, and forest) among predetermined locations on a private farm in Clay County, Mississippi, USA (3 replicates, 27 total cages). At civil twilight on 5 March 2020, the sUAS flew a systematic route over the cage area at 30 m above ground level, capturing thermal infrared photographs every 5 seconds. To assess detection, we distributed 57 photographs to 31 volunteers and asked them to assign a binary value for detection (1, 0) regarding covey presence in each photograph. Overall true positive rate was 0.551 but improved with increasing covey size. By vegetation type, simulated coveys in grass had the lowest true positive rate by photograph (0.403), followed by forest (0.562) and shrub (0.605). Results indicate that sUASs and thermal camera technology could be a viable method for surveying intact bobwhite coveys, especially if detection of smaller groups and those in denser vegetation improves. As this technology advances, we recommend that future research focus on evaluating the efficacy of this novel methodology through assessing the influence of weather conditions, camera specifications, flight speed, and altitude, as well as assessing machine learning for processing photos

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach. Supplemental files attached below

Expanding Predictive Assessment of Northern Bobwhite Covey Calling Rates to Incorporate Regional Effects

Many surveys based on discrete vocalizations make the invalid assumption that individuals present in the survey area are always available for detection (e.g., calling) during the survey period. Northern bobwhites (Colinus virginianus) are known to exhibit variable calling rates, particularly during autumn covey surveys. Adjustment of density and abundance estimates to account for calling rate may increase reliability of population metrics, and may increase our ability to effectively assess conservation management. Two previous independent studies across 4 regions used logistic regression to evaluate effects of weather, time, and density covariates on calling rates of radio-marked autumn bobwhite coveys. Results from these studies varied and there is uncertainty regarding application without further investigation into regional differences in calling rates. We combined these data sets comprising known calling rates of 279 bobwhite coveys in 4 regions (Florida, Missouri, North Carolina, and Tennessee) from 1998 to 2000. Observed calling rates averaged 69% over all sites, and ranged from 56 to 80% in the Florida and Missouri regions, respectively. We used binomial logistic regression to evaluate effects of region, adjacent calling coveys, weekly period, change in barometric pressure, percent cloud cover, temperature, and wind speed on covey calling rates. The top ranking model suggested strong effects of region and number of adjacent coveys on calling probability (P , 0.0001) with 42% model weight relative to other candidate models. Two competing models suggested inclusion of the 6-hr change in barometric pressure (0100 – 0700 hrs) (18% model weight) or weekly period (17% model weight) might also be appropriate. Validation using the best approximating model (region þ adjacent coveys) suggested calling probability estimates were within 6% of the observed calling rate in one region. This suggests the predictive model may provide a valid estimator of calling rate when applied to covey survey data in the appropriate region. However, there is uncertainty regarding application of region-specific model coefficients to survey data outside of these regions. If effects of region are important predictors of calling rate, managers must be cognizant of these prior to adjusting parameter estimates. Further, there is a research need concerning utility and ubiquity of calling rate predictors, particularly for regions that lack known calling rate data

Dataset for Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

Dataset from the results of an experiment to determine how three controllable factors, flight altitude, camera angle, and time of day, affect human identification and counts of animals from drone images to inform best practices to survey animal communities with drones. We used a drone (unoccupied aircraft system, or UAS) to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four GSD (ground sampling distance) values (0.35, 0.70, 1.06, 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals in drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday compared to morning and afternoon hours, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy compared to 90°, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations based on our results to design future surveys to improve human accuracy in identifying and counting animals from drone images for monitoring animal populations and communities

Individualized chiropractic and integrative care for low back pain: the design of a randomized clinical trial using a mixed-methods approach

<p>Abstract</p> <p>Background</p> <p>Low back pain (LBP) is a prevalent and costly condition in the United States. Evidence suggests there is no one treatment which is best for all patients, but instead several viable treatment options. Additionally, multidisciplinary management of LBP may be more effective than monodisciplinary care. An integrative model that includes both complementary and alternative medicine (CAM) and conventional therapies, while also incorporating patient choice, has yet to be tested for chronic LBP.</p> <p>The primary aim of this study is to determine the relative clinical effectiveness of 1) monodisciplinary chiropractic care and 2) multidisciplinary integrative care in 200 adults with non-acute LBP, in both the short-term (after 12 weeks) and long-term (after 52 weeks). The primary outcome measure is patient-rated back pain. Secondary aims compare the treatment approaches in terms of frequency of symptoms, low back disability, fear avoidance, self-efficacy, general health status, improvement, satisfaction, work loss, medication use, lumbar dynamic motion, and torso muscle endurance. Patients' and providers' perceptions of treatment will be described using qualitative methods, and cost-effectiveness and cost utility will be assessed.</p> <p>Methods and Design</p> <p>This paper describes the design of a randomized clinical trial (RCT), with cost-effectiveness and qualitative studies conducted alongside the RCT. Two hundred participants ages 18 and older are being recruited and randomized to one of two 12-week treatment interventions. Patient-rated outcome measures are collected via self-report questionnaires at baseline, and at 4, 12, 26, and 52 weeks post-randomization. Objective outcome measures are assessed at baseline and 12 weeks by examiners blinded to treatment assignment. Health care cost data is collected by self-report questionnaires and treatment records during the intervention phase and by monthly phone interviews thereafter. Qualitative interviews, using a semi-structured format, are conducted with patients at the end of the 12-week treatment period and also with providers at the end of the trial.</p> <p>Discussion</p> <p>This mixed-methods randomized clinical trial assesses clinical effectiveness, cost-effectiveness, and patients' and providers' perceptions of care, in treating non-acute LBP through evidence-based individualized care delivered by monodisciplinary or multidisciplinary care teams.</p> <p>Trial registration</p> <p>ClinicalTrials.gov NCT00567333</p