Geology topics

Ryan S. Miller

Publications and source records attributed to Ryan S. Miller.

11 recordsLinked to original sources

USGS2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

Ecology and Evolution↗

USGS2020

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Ecological Applications↗

USGS2019

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro‐ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro‐ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs ( Sus scrofa ), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental‐scale movement data within agro‐ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non‐agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro‐ecosystems.

California. Florida, Georgia, Louisiana, Missouri,↗

USGS2019

Risk factors and productivity losses associated with Mycoplasma ovipneumoniae infection in United States domestic sheep operations

Association of Mycoplasma ovipneumoniae with pneumonia in domestic small ruminants has been described in Europe, Asia, and New Zealand but has received less attention in the United States. In 2011, the US Department of Agriculture’s National Animal Health Monitoring Survey detected M. ovipneumoniae shedding in 88% of 453 domestic sheep operations tested in 22 states that accounted for 85.5% of US ewe inventory in 2001. We evaluated factors associated with M. ovipneumoniae infection presence and prevalence, and we compared health, lamb production, and ewe losses in in-fected and uninfected operations. M. ovipneumoniae detection was more common in larger opera-tions than in smaller operations. Both likelihood of detection (at the operation level) and within-operation prevalence were higher in operations with more open management practices than in oper-ations with more closed management practices. M. ovipneumoniae-positive operations showed sig-nificantly lower lambing rates and lower rates of lamb survival to weaning after accounting for dif-ferences in operation size and management practice. While its effect on any single rate was not par-ticularly large, in aggregate we estimated that M. ovipneumoniae presence was associated with an approximately 4.3% reduction in annual lamb production.

Preventive Veterinary Medicine↗

USGS2018

Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.

Transboundary and Emerging Diseases↗

USGS2017

Cross-species transmission potential between wild pigs, livestock, poultry, wildlife, and humans: Implications for disease risk management in North America

Cross-species disease transmission between wildlife, domestic animals and humans is an increasing threat to public and veterinary health. Wild pigs are increasingly a potential veterinary and public health threat. Here we investigate 84 pathogens and the host species most at risk for transmission with wild pigs using a network approach. We assess the risk to agricultural and human health by evaluating the status of these pathogens and the co-occurrence of wild pigs, agriculture and humans. We identified 34 (87%) OIE listed swine pathogens that cause clinical disease in livestock, poultry, wildlife, and humans. On average 73% of bacterial, 39% of viral, and 63% of parasitic pathogens caused clinical disease in other species. Non-porcine livestock in the family Bovidae shared the most pathogens with swine (82%). Only 49% of currently listed OIE domestic swine diseases had published wild pig surveillance studies. The co-occurrence of wild pigs and farms increased annually at a rate of 1.2% with as much as 57% of all farms and 77% of all agricultural animals co-occurring with wild pigs. The increasing co-occurrence of wild pigs with livestock and humans along with the large number of pathogens shared is a growing risk for cross-species transmission.

Scientific Reports↗

USGS2017

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Ecology Letters↗

USGS2016

Inferring invasive species abundance using removal data from management actions

Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine ( Sus scrofa ), using removal data from aerial gunning in 59 site/time-frame combinations (480–19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method, 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (more than three) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

Ecological Applications↗

USGS2016

“One Health” or three? Publication silos among the One Health disciplines

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains.

PLoS Biology↗

USGS2011

Targeted surveillance for highly pathogenic avian influenza in migratory waterfowl across the conterminous United States: chapter 12

Introduction of Asian strain H5N1 Highly Pathogenic avian influenca via waterfowl migration is one potential route of entry into the United States. In conjunction with state, tribe, and laboratory partners, the United States Department of Agriculture collected and tested 124,603 wild bird samples in 2006 as part of a national surveillance effort. A sampling plan was devised to increase the probability fo detecting Asian strain H5N1 at a national scale. Band recovery data were used to identify and prioritize sampling for wild migratory waterfowl, resulting in spatially targeted sampling recommendations focused on reads with high numbers of recoveries. We also compared the spatial and temporal distribution of the 2006 cloacal and fecal waterfowl sampling effort to the bird banding recovery data and found concordance between the two .Finally, we present improvements made to the 2007 fecal sampling component of the surveillance plan and suggest further improvements for future sampling.

Book chapter↗

USGS1994

Hydrogeologic framework and preliminary simulation of ground-water flow in the Mimbres Basin, southwestern New Mexico

The bolson-fill aquifer, the major water-yielding unit in the Mimbres Basin, southwestern New Mexico, ranges in thickness from 0 to about 3,700 feet. Recharge to the bolson-fill aquifer occurs by infiltration of ephemeral streams that cross the basin margin, infiltration from precipitation and streamflow, ground-water underflow from adjacent basins, and infiltration of springflow from adjacent bedrock units within the basin. Ground water generally flows southward from the northern highland areas of the basin. Ground-water discharge consists of pumpage from wells, transpiration by plants, outflow to playas and springs in the Los Muertos Basin in Mexico, discharge to the Mimbres River, and ground-water flow to the Mesilla Basin near Mason Draw. Before 1910, ground-water recharge and discharge were approximately equal; by 1975, however, about 75 percent of the 146,000 acre-feet withdrawn annually was ground water, most of it from aquifer storage. The transmissivity of the bolson-fill aquifer determined from aquifer tests and specific-capacity data ranges from 10 to 50,000 feet squared per day. Hydraulic conductivity, calculated from saturated thickness and transmissivity, ranges from 0.03 to 800 feet per day, with median values of about 18 feet per day in the Deming area and 6 feet per day elsewhere. Reported storage-coefficient values representing confined parts of the aquifer range from 0.00036 to 0.0036, and those representing unconfined parts of the aquifer range from 0.02 to 0.24. Water quality in the north and central parts of the Mimbres Basin is suitable for most uses. Due to its large salinity and alkalinity, some of the ground water in the south and southeastern areas of the bolson-fill aquifer may not be suitable for irrigation or domestic use. A preliminary two-dimensional digital model was constructed to evaluate ground-water flow in the bolson-fill aquifer. The model was divided into zones of uniform hydraulic conductivity corresponding to the major structural elements of the basin. For simulation purposes, hydraulic conductivity in the central part of the basin ranged from 2.2 to 4.4 feet per day, whereas locally along the edges of the aquifer less certain values ranged from 0.003 to 62 feet per day Analysis of the results of this predevelopment model indicated that use of the mountain-front recharge method overestimates total recharge and that evapotranspiration is substantial. The simulated total inflow was about 55 percent of that estimated in a water budget for the Mimbres Basin. Ground-water development between 1930 and 1985 was simulated using storage-coefficient values of 0.01 and 0.02 for the Gila Conglomerate, 0.04 to 0.17 for bolson-fill deposits, and 0.001 for bolson fill capped with lacustrine clay. The simulated transient water budget indicated that most of the water pumped by 1985 came from storage, and lesser but substantial amounts came from reductions in evapotranspiration.

New Mexico↗