Biology subjects

Miller, R. S.

Publications and source records attributed to Miller, R. S..

14 recordsLinked to original sources

bioRxiv2025

Spatial transmission dynamics of African Swine Fever in wild pigs: Sensitivity to epidemiological traits of different viral strains

African Swine Fever virus (ASFv) is a rapidly spreading animal disease with impacts on global economy and food security. Since the first known case of ASFv in Kenya in 1921, the virus has undergone significant evolution resulting in the emergence of 25 distinct genotypes. These genotypes have a wide variety of epidemiological traits, leading to uncertainty about how a genotype or variant of a genotype might spread in a population. Understanding the impacts of different epidemiological traits on the spread of ASFv is important for developing effective surveillance systems and control strategies. We developed a spatial epidemiological model of ASFv transmission in wild pigs, which contributes to ASFv persistence in countries where wild pig populations are widespread. We evaluated the impacts of a realistic set of epidemiological traits including incubation period, infectious period, disease-induced mortality rate, and length of immunity on spatial transmission dynamics under different ecological conditions for the host population (i.e. host density). We analyze effects of these conditions on outbreak metrics using boosted regression models with response variables: peak incidence, rate of spatial spread, seroprevalence and area invaded after one year, and probability that an outbreak will occur. We found that the infectious period of living individuals is the most important predictor at all pig densities for peak incidence and the area affected at one year, followed closely by the incubation period of the virus. We discuss considerations for surveillance and control strategies for different genotypes of ASFv.

ecology↗

bioRxiv2025

Determinants of variation in epidemiological parameters for African Swine Fever virus

African Swine Fever (ASF) is a highly infectious reportable disease of swine that can have devastating consequences for pork producers and consumers. ASF virus can persist in either domestic or wild swine through several epidemiological cycles. This has led to a wide variety of epidemiological traits, making it challenging to plan effective surveillance and mitigation strategies. Here, we compile and analyze data from experimental infections of ASF virus variants from genotype II that have been reported in peer-reviewed publications. We provide descriptive summaries of the distributions of epidemiological parameters such as incubation period, infectious period, mortality rate, and R0 and develop predictive models that explain the variation in these quantities based on detection assays and other experimental design features. Our results provide a comprehensive perspective of estimates of epidemiological parameters for ASFV, allowing for increased transparency in accounting for parameter uncertainty in ASF preparedness modeling. Our meta-analysis also provides insight on knowledge gaps and study design issues that could be addressed by future experimentation.

immunology↗

bioRxiv2025

Wildlife movement and contact responses to intensive culling: implications for disease control

Culling is frequently used to control animal diseases. Intensive culling can alter the movement behavior of surviving animals, especially in socially-structured wildlife species. These behavioral responses could have unexpected consequences on the spread of a disease. Thus, planning effective culling responses to diseases in wildlife hosts requires a thorough understanding of the potential impacts of culling on the target wildlife host species. We conducted a BACI design study of behavioral response to culling in wild pigs. We examined movement and contact responses in the populations using 122 GPS-collared wild pigs and three different culling methods (aerial operations, trapping, and an experimental toxic bait). Movement and contact metrics included home range area, net-squared displacement (i.e., home range shift), movement speed, distance, contact degree and contact duration. We observed increased movement distances during and after trapping treatments, and home range shifts and reduced area size after the toxicant treatment. We also observed increases in contact duration and number of unique contacts during trapping removals. Movement and contact responses varied by sex. Our results suggest that continued, intensive culling as with extensive trapping can substantially alter wild pig space use and contact. These behavioral responses could have important consequences for disease spread when managing an introduction of transboundary animal diseases or endemic diseases.

ecology↗

bioRxiv2025

Generalizing animal movement predictions across landscapes: a scalable framework grounded in empirical telemetry data

BackgroundUnderstanding animal movement across broad ecological contexts is essential for guiding management decisions in natural resource and animal disease management. However, most movement studies collect data over small spatial extents, which challenges using the data for landscape-scale management applications. MethodsWe collated relocation data from 564 wild pigs spanning 25 studies across 10 states in the United States. We derived net and daily displacement distributions for individuals in each of 4 seasons. We fit gamma distributions to displacement distributions and derived mean and dispersion parameters as metrics of displacement magnitude and variation. We modeled mean and dispersion variables using gradient-boosted regression, including landscape and weather predictors, and predicted daily and net displacement distributions across the species range. We used integrated step selection analysis to estimate habitat selection for wild pig groups sharing a common composition of habitat availability across the studied populations. ResultsFemale average ({+/-} 95% confidence interval) net displacement means varied by season, peaking in Jul-Sept at 1,152 {+/-} 140 m along with daily displacement mean distances of 650 {+/-} 41 m. Average net displacement means for males peaked in Oct.-Dec. at 1,651 {+/-} 160 m with daily displacement mean distances of 1,092 {+/-} 95 m. Extreme net displacement values ranged up to 9,649 m and 4,080 m for male and female, respectively, and extreme daily displacement distribution means of 6,616 m for males, and 3,855 m for females. Almost half of the individuals included a similar composition of habitat availability, regardless of study site location. Herbaceous wetland was the most consistently favored land type and was available for more than 70% of all individual pigs across all studies. Preference for other land types depended strongly on habitat availability. ConclusionsBy comparing movement and habitat selection across a large geographic extent, our work provides new insights about the environmental drivers and limits of wild pig movement behavior. Our workflow digests variation wild pig movement distributions and habitat selection into parameters that can be used to predict movement on heterogeneous landscapes for downstream applications such as invasive species elimination campaigns or disease management.

ecology↗

bioRxiv2024

Operational lessons learned from simulating an elimination response to a transboundary animal disease in wild animals

Transboundary animal disease (TAD) introductions can have myriad economic, ecological, and societal impacts. When TADs are introduced into wild species, rapid and intense control efforts to reduce wild animal host populations are sometimes needed to eliminate the disease and prevent endemicity and spillover to domestic animal populations. Yet, such intensive efforts are non-trivial, and the rarity of TAD introductions means that personnel rarely have direct experience with these types of operations. Thus, explicit assessments of operational challenges for these kinds of efforts can provide direction to build emergency response preparedness capacity. Here, we simulated a TAD control effort in response to initial detection of a hypothetical index case of a TAD in wild pigs (Sus scrofa) (e.g., African swine fever; ASF). We used three removal methods (aerial control, trapping, and an experimental toxic bait). Then, we conducted an after-action assessment to identify operational challenges for rapidly reducing a population of invasive wild pigs within a simulated outbreak zone. We also simulated carcass recoveries of dispatched pigs, similar to what might be conducted during a response to a TAD with carcass-based transmission (e.g., ASF virus). Here, we describe operational challenges identified during our effort, alongside technological development solutions and a priori strategy needs to improve TAD response operation outcomes.

ecology↗

bioRxiv2024

Torque Teno Sus Virus 1: A Potential Surrogate Pathogen to Study Pig-Transmitted Transboundary Animal Diseases

Understanding the epidemiology and transmission dynamics of transboundary animal diseases (TADs) among wild pigs (Sus scrofa) will aid in preventing the introduction or containment of TADs among wild populations. Given challenges associated with studying TADs in free-ranging populations, a surrogate pathogen system for in situ studies of pathogen dynamics would be ideal to elucidate how pathogens may circulate and be maintained within populations, how they may spillover into domestic populations, and how disease spread may be mitigated. We assessed the suitability of Torque teno sus virus 1 (TTSuV1) to serve as a surrogate pathogen for molecular epidemiological studies in wild pigs by investigating the prevalence, persistence, correlation with host health status and genetic variability at two study areas: Archbolds Buck Island Ranch in Florida and Savannah River Site in South Carolina. We then conducted a molecular epidemiological case study within the Archbolds Buck Island Ranch site to determine how analysis of this pathogen could inform transmission studies. Prevalence was high at both study areas (40%, n = 190). Phylogenetic analyses revealed high levels of genetic variability within and between study sites. Our case study showed that pairwise host relatedness and geographic distance were highly correlated to pairwise viral genetic similarity. Molecular epidemiological analyses revealed a distinct pattern of direct transmission from pig to pig occurring within and between family groups. Our results suggest TTSuV1 is highly suitable for molecular epidemiological analyses and will be useful for future in situ studies of transmission dynamics in wild pigs.

ecology↗

bioRxiv2022

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

Motion-activated wildlife cameras, or camera traps, are widely used in biological monitoring of wildlife. Studies using camera traps amass large numbers of images and analyzing these images can be a large burden that inhibits research progress. We trained deep learning computer vision models using data for 168 species that automatically detect, count, and classify common North American domestic and wild species in camera trap images. We provide our trained models in an R package, CameraTrapDetectoR. Three types of models are available: a taxonomic class model classifies objects as mammal (human and non-human) or avian; a taxonomic family model that recognizes 31 mammal, avian, and reptile families; a species model that recognizes 75 domestic and wild species including all North American wild cat species, bear species, and Canid species. Each model also includes a category for vehicles and empty images. The models performed well on both validation datasets and out-of-distribution testing datasets as mean average precision values ranged from 0.80 to 0.96. CameraTrapDetectoR provides predictions as an R object (a data frame) and flat file and provides the option to create plots of the original camera trap image with the predicted bounding box and label. There is also the option to apply models using a Shiny Application, with a point-and-click graphical user interface. This R package has the potential to facilitate application of deep learning models by biologists using camera traps.

ecology↗

bioRxiv2021

Transient population dynamics drive the spread of invasive wild pigs in North America

Invasion of nonindigenous species is considered one of the most urgent problems affecting native ecosystems and agricultural systems. Mechanistic models that account for short-term population dynamics can improve prediction because they incorporate differing demographic processes that link the environmental conditions of a spatial location explicitly with the invasion process. Yet short-term population dynamics are rarely accounted for in spatial models of invasive species spread. Accounting for transient population dynamics, we predict the population growth rate and establishment probability of wild pigs following introduction into any location in North America. We compared predicted population growth rate with observed geographic rates of spread and found significant relationships between the annual rate of spread and population growth rates. We used geospatial data on the distribution of mast producing tree species (a principle forage resource of wild pigs) and agricultural crops that can replace mast in their diets to predict population dynamics using transient population simulations. We simulated populations under different initial population sizes (i.e. number of introduced individuals, often termed propagule size) and for different amounts of time following introduction. By varying the initial population size and simulation time, we were able to identify areas in North America with high probability for establishment of wild pigs if introduced. Our findings can be used to inform surveillance and removal efforts to reduce the potential for establishment and spread of wild pigs.

ecology↗

bioRxiv2020

rapidPop: Rapid population assessments of wildlife using camera trap data in R Shiny Applications

O_SCPLOWRAPIDC_SCPLOWPO_SCPLOWOPC_SCPLOW is a new R package for implementing occupancy models for Rapid Population Assessments (RPAs) with data from camera traps. RPAs are designed to provide quick assessments of a density index so that users can identify relative changes in density associated with changes in conditions (Schlichting et al., 2020). For example, users may want to assess if there was a change in density after an effort to cull an invasive species. O_SCPLOWRAPIDC_SCPLOWPO_SCPLOWOPC_SCPLOW provides a Shiny Application for running occupancy models with the option to include the effect of parameters on occupancy (e.g., before or after a culling operation).

ecology↗

bioRxiv2020

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

O_LIMotion-activated wildlife cameras (or "camera traps") are frequently used to remotely and non-invasively observe animals. The vast number of images collected from camera trap projects have 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. C_LIO_LIWe 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." C_LIO_LIOur 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. C_LIO_LIOur 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. C_LI

ecology↗

bioRxiv2019

High throughput small molecule screening reveals NRF2-dependent and -independent pathways of cellular stress resistance

Biological aging is the dominant risk factor for most chronic diseases. Development of anti-aging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen in C. elegans. Transcriptomic analysis implicated Nrf2 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Molecules that conferred stress resistance also induced cellular inflammatory pathways, and other core pathways such as AMPK signaling. Small molecules identified in this work may represent attractive candidates to evaluate for their potential pro-health and pro-longevity effects in mammals.

cell biology↗

bioRxiv2019

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface

AO_SCPCAPBSTRACTC_SCPCAPManaging pathogen spillover at the wildlife-livestock interface is a key step toward improving global animal health, food security, and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework may be a useful step in advancing how we classify and prioritise responses to novel pathogen spillover threats, and evaluate current management actions for pathogens emerging at the wildlife-livestock interface.

epidemiology↗

bioRxiv2018

Determinants of invasive species policy: print media and agriculture determine United States invasive wild pig policy

Conflicts between wildlife, invasive species, and agricultural producers are increasing. Although direct management actions taken to mitigate these conflicts remain controversial, most stakeholders agree that better policies are needed to balance socio-economic considerations with invasive species management, wildlife conservation, and agriculture. However the interaction between societal and biological drivers that influence human-invasive species-wildlife conflict mitigation policy is poorly understood. We identify factors influencing policy leading to the establishment of a new federal program to control invasive wild pigs in the United States. We fit generalized linear models relating frequency of congressional policy activity, such as congressional hearings and reports, to frequency of print newspaper media and percent of the U.S. agricultural industry co-occurring with invasive wild pigs for 29 years preceding the establishment of the federal program in 2013. Our models explained 89% of the deviance in congressional policy activity indicating a strong linkage between congressional invasive wild pig policy activity and predictors representing the number of negative of newspaper articles, geographic distribution of print media, and percent of agricultural producers co-occurring with invasive wild pigs. These effects translated to 3.7% increase in the number of congressional policy actions for every additional five states with negative news media. Invasive wild pig congressional policy activity increased 6.7% for every additional 10 negative newspaper articles. Increases in co-occurrence of agriculture and invasive wild pigs had the largest effect, for every 1% increase in co-occurrence there was a 41% increase in congressional policy activity. Invasive wild pig congressional policy activity that explicitly addressed livestock increased at nearly twice the rate of policy activity addressing crop agriculture. These results suggest that agriculture and media coverage may act as determinants for invasive species policy. Our approach may provide early insight into emerging policy areas enabling proactive policy development by agencies or early engagement by scientists to find solutions before the policy area becomes grid locked. Our results can also support policy and program evaluation providing a means of determining if the implemented policies match the original policy determinants ensuring best alignment with public, environmental, and stakeholder interests.

ecology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗