Biology subjects

Snow, N. P.

Publications and source records attributed to Snow, N. P..

7 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↗

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

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↗

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↗

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↗