Biology subjects

VerCauteren, K. C.

Publications and source records attributed to VerCauteren, K. C..

10 recordsLinked to original sources

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↗

bioRxiv2024

Comparing efficiencies of population control methods for responding to foreign animal disease threats in wild pigs

Introductions of foreign animal diseases (FADs) into free-ranging wildlife can be difficult to control and devastating for domestic livestock trade. Combating a new FAD introduction in wildlife with an emergency response requires quickly limiting spread of the disease by intensely removing wild animals and recovering their carcasses for proper disposal. In the case of African swine fever virus (ASFv) in wild pigs (Sus scrofa), which has been spreading in many regions of the world, there is little information on the time- and cost-efficiency of methods for intensively and consistently removing wild pigs and recovering carcasses in an emergency response scenario. We compared the efficiencies of aerial operations, trapping, an experimental toxic bait, and ground shooting in northcentral Texas, USA during two months in 2023. Removing and recovering carcasses of wild pigs averaged a rate of 0.15 wild pigs/person hour and cost an average of $233.04/wild pig ($USD 2023) across all four methods. Aerial operations required the greatest initial investment but subsequently was the most time- and cost-efficient, costing an average of $7,266 to incrementally reduce the population by 10% including recovering carcasses. Aerial operations required a ground crew of [~]7 people/helicopter to recover carcasses. Costs for reducing the population of wild pigs using trapping were similar, although took 13.5 times longer to accomplish. A benefit of trapping was carcass recovery was incorporated. Toxic baiting was less efficient because carcass recovery required substantial time, and we removed very few wild pigs with ground shooting in this landscape. We recommend combining aerial and trapping methodologies to remove wild pigs and their carcasses efficiently and effectively during a FAD response. Overall, our findings can inform the preparation of resources, personnel needs, and deployment readiness for FAD responses involving wild pigs.

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↗

bioRxiv2023

What is Known, Unknown, and Needed to be Known about Damage Caused by Wild Pigs

Damage assessments provide evidence for initiating and evaluating management programs that protect natural resources and human livelihoods against invasive species. Wild pigs (Sus scrofa) cause extensive damage in their native and non-native ranges, but the extent of current damage and efficacy of management of the damage (i.e., population control of wild pigs, exclusion fencing, etc.) remains poorly described. We conducted a systematic review of physical damage caused by wild pigs to summarize what is known and identify knowledge gaps for damage assessment. Wild pig damage assessments have been overwhelmingly qualitative (84% of studies) and measured differently across studies, which prevents the determination of typical damage amounts to a particular resource and comparison across studies. Key priorities going forward are to standardize damage assessments quantitatively and measure population density (or index of such) of wild pigs concurrently with damage assessments to determine the relationship between damage and population levels. We provide a framework for inferring damage in new areas and assessing the benefits of management - to evaluate and optimize landscape-scale management programs. Overall, we recommend future studies strive for: 1) report the amount of damages in a standardized fashion (e.g., area damaged/area surveyed), 2) evaluate and report the amount of damage relative to the density of wild pigs, and 3) when reporting economic costs of damages incurred and management actions, describe the economic valuation method used along with the year of reference for the valuation. Capturing these elements are necessary steps to predict the benefits of management for an area with particular profile of resources and wild pig density, even in areas where damage assessments are not available. Meeting these criteria with allow for more generalizable results that can inform managers across the nearly global distribution of wild pigs

ecology↗

bioRxiv2023

Wildlife vaccination strategies for eliminating bovine tuberculosis at the wildlife-livestock interface

Many pathogens of humans and livestock also infect wildlife that can act as a reservoir and challenge disease control or elimination. Efficient and effective prioritization of research and management actions requires an understanding of the potential for new tools to improve elimination probability with feasible deployment strategies that can be implemented at scale. Wildlife vaccination is gaining interest as a tool for managing several wildlife diseases. To evaluate the effect of vaccinating white-tailed deer (Odocoileus virginianus), in combination with harvest, in reducing and eliminating bovine tuberculosis from deer populations in Michigan, we developed a mechanistic age-structured disease transmission model for bovine tuberculosis with integrated disease management. We evaluated the impact of pulse vaccination across a range of vaccine properties. Pulse vaccination was effective for reducing disease prevalence rapidly with even low (30%) to moderate (60%) vaccine coverage of the susceptible and exposed deer population and was further improved when combined with increased harvest. The impact of increased harvest depended on the relative strength of transmission modes, i.e., direct vs indirect transmission. Vaccine coverage and efficacy were the most important vaccine properties for reducing and eliminating disease from the local population. By fitting the model to the core endemic area of bovine tuberculosis in Michigan, USA, we identified feasible integrated management strategies involving vaccination and increased harvest that reduced disease prevalence in free-ranging deer. Few scenarios led to disease elimination due to the chronic nature of bovine tuberculosis. A long-term commitment to regular vaccination campaigns, and further research on increasing vaccines efficacy and uptake rate in free-ranging deer are important for disease management.

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↗

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↗

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.

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